This recorded webinar will introduce you to India’s leading Applied Data Science Experts and your future mentors of IIT Madras and TalentSprint’s Applied Data Science and Machine Intelligence Programme. Take a look.
This recorded webinar will introduce you to India’s leading Applied Data Science Experts and your future mentors of IIT Madras and TalentSprint’s Applied Data Science and Machine Intelligence Programme. Take a look.
IIT Madras & TalentSprint - Applied Data Science & Machine Intelligence Program
So Good evening, everybody. Thank you so much for joining in. And welcome this we've not been v i don't think this is we've done a webinar in a while for the applied data science and machine intelligence program that we have launched. So, you know, I'm very excited today we have professors, Ravindran and Professor a room with us today. And they will be sharing some of their perspectives on the program. So what we're going to do is I have a very small presentation, it's not even a presentation. But if you really look at it, you know, my name is Ernesto ceria. I had admissions for talentsprint programs that we run with multiple organizations, you know, and leading institutions, like IIT Madras, for example. And today, I'm going to be in conversation with both the professors and we're going to talk a little bit about the program, I have been given to understand that, you know, it's a mix of people who have already enrolled in the program, as well as people who are considering enrolling in the program. So what we're going to do is that, we will have a set of questions. And these are some questions that we keep getting from a lot of you who want to understand a little bit more about the program, as well as we have, you know, we are also going to be taking your questions in as a part of the session. So I would request all of you to hold on to your questions, we will have a dedicated audience q&a towards the end of the session where we will try and answer questions that we have not been able to address as a part of the session. And so just hold on to that. The other thing is that if you really look at it, this is a set of programs This is one amongst a series of programs that we have launched with many leading education institutions in India. And and this is the first program with IIT Madras for us, so we are incredibly proud of that, you know, there are so many other programs that we run with all of these institutions. So what I am going to do is, you know, I'm now going to just stop the screen share for one and we will get a small introduction from both the professors who have decided to join us so welcome Professor Ravindran welcome Professor Rajkumar.
Why don't we start with Professor Raj Kumar? And, you know, why don't you tell us a little bit about yourself to the people who have joined in and then we can go to Professor Ravindran.
You are on mute.
Okay, can you hear me now? Yes, we can. Thank you. Okay, thanks.
My name is Ron Rajkumar. I'm in central 70, Department of Computer Science and Engineering at IIT Madras. So I joined IIT Madras in 2019. And I've been here for a couple of years now. So before that, I did my PhD from Indian Institute of Science, which was again in machine learning and artificial intelligence. Then I worked in the industry for a couple of years, with Xerox research labs, Bangalore, where again, I looked at a lot of industry related problems in transportation in healthcare, crime analytics, and so on. Now, before I moved to academics, so my areas of interest in research are in machine learning,
data mining, artificial intelligence, and in a broader sense, also the both the theoretical and practical aspects of it. So that's a short introduction. Thanks.
So Hello, everyone. Good evening. I hope all of you are keeping safe. I'm loving. I'm also a faculty in the computer science and engineering department at IIT Madras. And I also had the Robert Bosch center for data science and AI, which is an interdisciplinary AI center that has been in operation since 2017. Right and this particular program is going to be offered by faculty from the center.
And so I have been with IIT Madras since
2004. And before that, I did my PhD at
University of Massachusetts at Amherst, and then before that, I had done my masters in industrial science as well. Right. And so, my areas of interest are largely in machine learning, and AI and more recently, I've started looking at, you know, aspects of how to get AI systems deployed in practice. And I do a lot of work in collaboration with industry.
I recently finished a sabbatical in industry and this came back. And so then not just looking at the theoretical aspects of AI and machine learning, but also looking at, you know, what would it take to deploy, and especially deployed things in an Indian context, there are so many unique challenges to, you know, getting the machine learning systems work in India. So to some degree don't understand that. Well, right. So the industry doesn't have mature researchers to look at it, and academia doesn't have enough of an industry Connect. And so, we are trying to at the center, we are trying to address that as well.
Right. Thank you, professor. So, which, which kind of gets me, you know, to the first question that is there and you know, either if you can take a crack at this, you know, Robert birth center at IIT, Madras, so, what exactly does Robert birth center do? And what kind of why did we look at before launching a program like this, what led to this program being launched?
Actually, okay, I think I think I should take a crack at it, given the head of the center. So the Robert Schwartz center is actually an interdisciplinary center, one of the very few
really hardcore interdisciplinary centers in AI in internationally, not just in India, right. So so we have people from 10 different academic departments who are part of the Center on all the engineering disciplines and people from management school, from mathematics, people from even humanities are part of the center, right. And so the idea here is that now, there are a lot of fundamental research that is happening in AI. But there are also a lot of issues that come when you look at, you know, areas of AI, cutting across all of these
departments, all of these verticals, right. And then the whole idea behind the center is to take advantage of synergies that arise when you look at problems cutting across verticals. So for example,
our center has a lot of strength in looking at what is called network analytics, right? So you look at, you know, connected entities, like it could be traffic flowing, flowing in a city, right? So think of that as a connected entity, or people on social networks, or people on a telecom network, right. So there are many, many domains in which these kinds of networks arise, right. So whether it's like Facebook, traffic, or whatever. And it turns out that when you start peeling away the the areas, right, and then get down into looking at what are the techniques at work, it's a similar kind of techniques work across the spectrum. So that is one of the things that we are leveraging from the center. So you asked us what we do at the center, we do both fundamental research, as well as looking at cutting edge applications of these, right, that's all that I mean, I have a slide when I talk about the center. And then we have this one slide with like 40 different industrial collaborators that the center works with, right then and then and then we have another slide that looks at all the international academic collaboration that we have. And that spans also almost a good fraction of the globe. So both on the clinical side, as well as the as a model, applied side, right, so Center does a lot of work. The whole idea is to keep struggling this as we go along. I don't want it.
Yeah, so I think that is one of the main reasons why a program like this from such a center makes a lot of sense, right? So because the faculty in the center have varied experience in different industrial domains. I mean, they work with a lot of industry partners, and so on. And so they understand the challenges that somebody who's in the industry would actually, you know, face from machine learning context, and we thought that we can have a program which can fine tune, you know, as opposed to several other programs, we can come up with a program with the, with the not just machine learning, but then with an industry focus, right. So I think RBC design is a great place to have such a program. Yeah, absolutely. Which kind of, you know, gets me it's a very interesting set of I don't know how many people in the audience have, you know, visited the Robert birch Center website of IIT Madras, but you know, there are a great set of companies that you know, you're working with, so just just for the interest of everybody you know, who's joining in, I would want you to kind of talk a little bit Professor Ravindra, about the kind of companies that you're working with the kind of collaborations you know that that or any interesting use cases or areas where you know you're working which which would I'm sure you know, what you're doing over there good kind of flow into a program like this as well. So So can we can we just have some idea of the kind of, you know, companies that we are working with, because you know, like the name says this is an applied data science and machine intelligence program. So I'm sure people are really interested in knowing about
The work that's going on. So the one thing that I wanted to point out as part of the previous answer, can you finish that and then get to you.
So if you actually look at the faculty who are teaching this course, right, you'll be very surprised to find out that it's just not computer science, right? So there are people from civil engineering, there are people from mechanical engineering, there are people from the design department, where are people from, you know, biotechnology, all the people are coming together. And so not only are these people, you know, knowledgeable in AI, but knowledgeable in applying AI to very specific.
Somebody is asking questions, we for the time being perfect, okay, we'll Well, we will ignore the questions. Okay, fine, fine.
So, so, so, so that's the point I wanted to make, not just that it's not just computer science people, you know, talking about other applications, people from those domains, we're going to talk about how to use AI ideas, and machine learning ideas in their domains as well. So that's one thing I want to point out.
Everybody is all the faculty of the program or IIT Madras faculty, the faculty of the program, right? Yeah, except for the bridge module where bureau talentsprint will be taken care of this will be the foundational stuff everybody, you will be taught by faculty from IIT, Madras.
Can you share one slide? Absolutely. The one that I was telling you that I use when I talk, right, absolutely, yeah, there's a lot of companies.
And so you're asking me, what are the companies you've worked with? Well, it's across the gamut, right? So you can see that there are many Indian companies as well. And there are also a lot of multinationals. Right. So, so this, so we work across the board, right? So some of the verticals, maybe I can mention that. Some of the verticals that we work in and or you know, in manufacturing analytics, right, so we look at looking at prediction, well first principle models that people use for modeling large scale systems, and also look at how to improve those with data driven applications, right? We do a lot of work in the FinTech space, right, looking at things like credit score in customer behavior modeling, like risk prediction, and all that, right. So we work in system quality and healthcare with a lot of barriers, both non governmental bodies as well as companies, right. And then we do a lot of work in the Smart Mobility space, and also looking at things like water distribution, power grids, and as well as pollution modeling, and so on so forth in the Smart City space. So these are the various verticals we look at. So you can look at across the board lectures, there are people like Applied Materials, Dr. Hall, and the G where we work with them on Ashok Leyland, when we were doing projects on
smart manufacturing and manufacturing, analytics, and so on, so forth, send Cobain kill it and call all these companies are in that space. And then there are
no labs, right, like Jackson labs and
then the genome India, there are 10k India initiative and a variety of these kinds of nonprofit organizations with whom we work on. I know the both the system biology as well as the healthcare model, right. And then the other people like society general who work with us and also dwara Trust, who do a lot of work with us on the FinTech side of things. And it says Indian Railways here but we actually working with the rival Hospital in terms of looking at some healthcare solutions and multiple players in that space right there. Well, we can be worked with healthcare. In fact, one of the things that we do is we work with Oh, no ambulance emergency response service in Chennai, and helping them improve their response time, their deployment of resources, and these are projects that are actually going to be implemented, deployed and deployed on on the ground. I mean, I can just go on but they're just as you can see, our collaborators span. industry verticals also span sectors in span government bodies, not NGOs, not for profit, as well as multinationals and Indian companies. If I may ask you this one very interesting in Northwestern University.
So what's the what's the collaboration with
the reason Northwestern appears here and not among our India academic collaborators? I mean, our academic collaborators list is large, right? I mean, so not as it appears here, mainly because they have as a contractor as an external researcher. Okay, so there are certain projects in northwestern for which they needed particular expertise, which they contracted to. So the relationship with Northwestern is, is a kind of industrial relationship. But the northwestern is not the only university we collaborate with. So we work with, I mean, across the board we work with we do a lot of work at Purdue, Ohio State, Northwestern UT Dallas, Mila many collaborators in Europe. Right. So So, so to look at my job this is always telling you the
regrets of Japan, Australia and Singapore.
A lot of European countries, Canada, us, many collaborators around the globe, these are all academic collaborators. Excellent. And a follow up question here. So is it is it safe to say that, you know, aspects of all these collaborations what we are working in flow into the curriculum of the program?
The curriculum is in two parts, I should get an answer this but like, just give my take on it. And then he can expand on the curriculum is really two parts. So that is the fundamentals of ml fundamentals of machine intelligence fundamentals of
data science, right? That that is
very, very strong. Okay. Yeah. So the first two parts, like one, two and four, right, 124. And six, basically, are the fundamentals of research, right? So some of it is very basic, foundational things, right. But then three and five is bad, we really are bringing in a lot of our collaborations, and, you know, adding value to this program by looking at our interaction to the industry. And so 124 and six, which are more of the foundational aspects of the course, again, brought from all our extraordinary and and teaching experience, as well as our research experience.
Yeah, perfect date. So as Ravi mentioned, right, so you have one, two, and four, and six, which are foundational, and three, and six are where we will bring in industry collaborators. And the idea is that depending on the cohort, and you know, the type of interest that the cohort in general has, we can go back and ask the corresponding industry folks to come and give me some use case studies as well, right. So for instance, if predominantly a set of people in a cohort are interested in, let's say, manufacturing, data analytics, or smart city steps analytics, then, I mean, as you saw there, our BCD side has a long list of collaborators. And we can always bring in expertise from that field as well. It's of course, there are faculty here who are also collaborating with them, we'll also be talking about
these real world use cases. In addition to that, we will also have some industry collaborators also we'll be getting some of the sessions, right. So in that sense, this is unique in the sense that we can kind of also tailor make it to whatever extent possible to the cohort in terms of the industry, industry level use cases.
Very interesting. And just for everybody's, you know, understanding we've we've we've brought we were you know, we have fielding about most of the seats that we would want to fill in for the first cohort. And I believe we have people from about 11 to 12 odd industries, currently, we still have some seats that are available. And it's a pretty interesting mix, I was looking at some high level data, I think the average years of experience is somewhere about eight, eight and a half years now. And while initially we had started off with the program did the idea that you know, we want to restrict it to about five years, but then we got humongous interest from people who have more than five years of experience, it's probably because, you know, it's coming from, you know, IIT Madras and Robert percenter in, you know, more specifically. So, what So, what we have done is and one of the questions that we've all been receiving is, you know, people want to understand a little bit in depth into what we plan to cover in the curriculum. So, one of the things that we are doing is that yes, you know, we will have a very interesting or a very diverse set of people who will be joining in So, in that context professors maybe professor who maybe want to do that, can you just tell us a little deeper into you know, all these six aspects of the curriculum and and just talk a little bit about what people can expect you know, the kind of rigor that we are planning to you know, have as a part of the program. So, what are they signing up for? Sure. So, let me go over these six modules one by one and then give a brief peek into each of these. So, the first module that you see is foundations of data science. So, you know, the data science has as a as a field relies on a lot of fundamental mathematical ideas and we understand that people who join the program may not be abreast with a lot of these. So, the goal here is to make sure that we get get you to a level foundational level where you can then later on, you know, understand all the algorithms of machine learning and so on, which is the second module, where what we will do is we will look at the, the basic paradigms of machine learning things like supervised learning, unsupervised learning and several other paradigms which are fundamental to several machine learning problems. And we will see several algorithms, a state of the art algorithms, algorithms that people use in the industry.
See all the time. But then the second module would focus on the algorithmic aspects of it. So the more foundational aspects of it, we are not going to think about the industry level challenges in Module Two, right? So we will first introduce what are the algorithms themselves in Module Two. And once you go to Module Three, then we take up a real world use case. And now the moment you go into the real world use case things are going to be entirely different, right. So you need to think about a lot more things while you actually take this algorithm and deploy it in the industrial sector. Right. So the pipeline is entirely different, right. So the algorithm just fits into one part of the entire machine learning pipeline, in an industry level setup, right, so you have the data collection part of it, you need to understand data pre processing, and then see what other possible challenges might might appear depending on the specific use case that you look at. And what we plan to do here is
also have a mix of industry level seminars, use case discussions. And also, you know, join discussions, I mean, people can form groups, and then try to understand the practical take of one particular use case and try to fall deep into it, depending on the cohort and the interest about which problem you want to look at, and so on. So that would be module three. Now, module four is interesting, because module two talks about the classic machine learning algorithms. Whereas in today's world, what has happened in the last maybe 10 years or so is that there has been, I mean, a sweep in machine learning community where deep learning has taken over machine learning, so to say. So deep learning is a subset of machine learning. It's a type of,
it's a type of, it's a paradigm, it's an, it's a different way of looking at machine learning problems. And most of the successful you know, big data algorithms today are deep learning based. And because this is an applied data science program, it is important to understand the foundations of deep learning as well. And that's what we will do in module four. So again, with deep learning a new set of challenges appear, right, so which which are not really associated with the traditional machine learning type of use cases. So deep learning means big data, big data means you have challenges of scale, you have challenges with respect to training, and so on, right, so so now we look at some real world challenges with respect to deep learning in, in module five, with respect to applications to vision applications to natural language processing, and perhaps speech and other things as well. So that would be module five. And what we will do in module six is, well, all this is fine. So where you have a bunch of data, and then you want to do some kind of machine learning over this data, you can use classical algorithms, you can use deep learning based algorithms, all that is fine. But what might happen in a real world scenario, where you really want to deploy these algorithms is that you are not going to be I mean, there are several scenarios where you won't see that all the data together, right. So data is going to appear one at a time. And and then you will want to, you know, learn over time in an online fashion. So this is what is called a sequential learning, which is something that is not always covered in our machine learning course, though, I think it's very fundamental. And it should be and that's why we put that here. So it has its own associated challenges, how do you learn over time, right? So I mean,
so let's think of this as, as a machine learning application in your form, right? So depending on how you interact with the form, it's going to kick it's the application is going to learn as you interact with the form for instance right. So which means that that knowledge of learning in a sequential fashion has to be built into the system. And that is what we will look at
module six, and I think what is also there after module six is the capstone project, perhaps that is not listed here, which is where all of these things will culminate right, so you have a project where you have to use the participants would be using all the knowledge that they've gained over these six modules and then build from scratch some system keeping in mind all the potential industry level challenges that might occur. So that at the end of that they have built from scratch some machine learning system which is workable, which can well at least in a prototype can be industry ready.
Absolutely. Yeah. So the capstone party we didn't cover and also the the pre learning modules that is there which we are going to you know give access to people we have the orientation from the top on the 25th of this month, and then you will have a couple of weeks of access to pre learning materials and then you know, you will the the classes on the preparatory modules, the bridge module, as we like to call it will start. So we will cover that as well. I thought, you know, we just wanted to focus on the main part of the curriculum. And thank you so much
for, you know, the nice detailed explanation that's there.
So, if I really look at it and we get this question a lot in terms of you know machine intelligence, so, what is machine intelligence? We I am sure a lot of people would have asked us this question you know, why is it what is machine intelligence? Why do we call it machine intelligence and not machine learning as a part of that any of you want to take a crack at that and help us out with bureau clarifying that
maybe I can maybe we can start so, machine intelligence I mean, basically the the, so, philosophically the holy grail of man has to be mean tried to build in some sense a computer that will mimic the human brain right. So and So, people have been trying to think about this for like decades now. And what what we mean by this broad term machine intelligence is you know, some have tried to mimic aspects of the human brain right. So, with respect to either the visual part of it with respect to the speech part of it with respect to the text part of it, these are things that human beings have learned inherently I mean, this is what we mean by in some sense intelligence, right? So, you look at a tree and then you recognize that this is a tree right? So that is, in some sense, intelligence, and now how can you make your computers work, right. So the easiest of things for human beings become so tough for the computers to learn. And so, it needs, you know, to incorporate intelligence into the machine is going to need a lot of thought and so on. And this broad area of machine intelligence thinks and comes up with, you know, algorithms where the machine learns from experience, how to, you know, act intelligently, so to say, so, at a broad level, that could be, of course, there are details, but Absolutely, thank you so much. So, we will also talk a little bit about UI I see a lot of questions, which are already coming in, but if you really look at, you know, the market is flush with, you know, programs on data science, etc, etc.
How do you, in your words, as the creators of the program, how would you say that this program is different from you know, other programs that are available in the market?
Sure, I let me take a criteria, we actually have touched upon various aspects of it already, right? You tell me which other AI or machine learning or machine intelligence program out there has so many people from the domain expert domain expertise,
right. So the first thing is, our focus is on not just giving you the fundamentals, not just saying, okay, here is a Python library, and you can run it on this data set with $100 elements, and then you're done, which focus is not to, you know, give you a certificate on paper, our focus is to make sure that when you finish this program, we are able to solve real problems. And that is why there are so much sugar, no emphasis on, if you look at the early split up, also, you'll see that there's a significant amount of hours that are spent on use cases, as opposed to many of the other programs that are out there. And I think that is a significant differentiator of this. So the focus on you know, applied, so being able to build applications out of these algorithms, these concepts that you learn, not leave it up the constant trouble.
Absolutely. I really want to add something to that. I
mean, yeah, so I think as Sarah mentioned, right, so, we've touched upon a lot of things why this program is kind of different from a lot of programs is is is because of the you know, the the industry focused
focus of the program itself and also because of the you know, the uniqueness of Robert Byrd center itself right. So, in the sense that this is the center, I mean, one of our times center in the country where so many faculty from varied domain expertise, participate in a program like this right. So, I mean, I would have liked to have a program to have learned from program like this when I started mission learning. So in the sense that you get so much perspective when you when you listen to people who have worked in that particular area, right so I mean, for instance, I have not worked in like, for instance, a biology based application or or a smart city based application but then I mean, I might come and teach a class but then that's not going to be so useful as opposed to somebody who has done research who has worked in these areas and coming and giving their perspective about what might be the problems in such a such a domain right. And this is something unique that this program offers which I think not so many other programs have abs. Yeah. So one other thing which you know, which we you kind of spoke about it. You know, when we were starting the webinar, you You said that, you know we are going to customize or depending on what
The class wants you know we will have industry mentors coming in and talking about what's happening in the industry in terms of that so I think that's also something which is very unique preparing the content of the course is kind of going to be customized a little bit if I'm not you know promising too much in terms of what the class wants so so that's that's something which is very unique about it as well. And we've been talking about faculty so you know, you are two of you will be teaching the program definitely, I believe there are a couple of three two or three other faculty also will be teaching the program correct. In fact, more than that, right so there are so in addition to me and personally so there are six more faculty will be teaching this program. So right from So should I go with the names? I mean, we won't. We can talk a little bit about that. I mean, the more people know about it, the better. Sure. So, Prosser Nandan so that's number two is from the
Management Science Department who set a lot of experimental design Applied Statistics machine learning so he will be also be part of this. Professor Karthik Rahman, who is who works on biological networks healthcare and problems related to that. So he would give his perspective on computational systems biology, healthcare, how machine learning can be applied to healthcare. Also, Manju Sinha Dr. Nieto but who is again the biological engineering faculty. So he will also be giving his own perspective in this course. We have Dr. Gita, Krishna and Rama de Ray. So who works a lot on you know, traffic modeling transportation networks and so on, pedestrian safety and things like that. So he would be also giving us perspective about smart cities and things like that. We also have
Balaji Sreenivasan Professor Ganapathy Subramaniam person, mitesh, copra, who are all leading researchers in deep learning, so they will also be giving us they'll, they will take us through the deep learning part, which, for instance,
so Professor Ganapati, works on patient part of deep learning a lot of the mutation works on the text part of the deep learning a lot. And I mean, we have a mix of people who can work experts in different domains and machine learning, and
all of them are coming together for this course. Oh, that's, that's a very general unique set of people with great competence.
Professor Ravi, you you wanted to add something to that? No, no, no, that's, that's a good list. So I just wanted to say that kind of beauty is in the engineering design department. So that's one department he forgot to mention. Biology from mechanical engineering.
Right. So also, as a part of, you know, your interaction with the industry, you know, I know you you advise a lot of companies, etc, in terms of their AI strategies, etc, if I can put that as a broad thing of what you do. So what is it that the industry is looking at, especially when it comes to young talent, I know a lot of the audience over here will be, you know, people who are probably stepping into their careers, they would have spent about a couple of years etc. Broadly, you know, what do you think this audience should be focusing on in terms of competence, beats technical competence, or beat some other competencies that they should be looking at to be valuable in the job market, so to speak. And before professor asked this question, we'll start looking at the q&a section also, of the webinar, you know, we have about 2025 1520 more minutes to do that. So you can start sending your questions wherever it's come in, start answering that as well. And I believe, you know, this is one of the biggest questions that is there is, you know, how will this program helped me become more, you know, more wanted in the industry, so to speak, not from a negative connotation?
I mean, I can talk about it, but you have a person who actually was in the industry, hiring people for a few years. You should ask him, what is it that he would look for if he is hiring somebody for Xerox research? in AI background? Yeah. So what? Okay, so what I would look for, right, so
So, so not them in today's world rich people call this data scientist, and that's a term that has been abused a lot, right? So anybody who says the data
doesn't really understand the science behind data, I would think it's not so in the sense that what people typically know, is just a black box. Understand they have a blackbox understanding of data science, right? So I mean, knowing the Python library
background work is not really does not really make one take you as a data scientist, what we want to do in this program is to, you know, if you want to become a data scientist tomorrow, right, so you really have to understand what goes inside this black box. And the reason why you need to understand this is so as, as in my experience in the industry, right, so the the problems that one typically faces in the industry, you don't, you cannot, I mean, it's very uncommon that you can take off the shelf classifier or off the shelf algorithm directly applied, right? So that that typically doesn't work that way. So what you need to understand is, you know, unless you know the mechanics of how things work, right, so it is very hard for you to, you know, custom make these things for the industrial application that you have at hand. And to do that you need to really understand these algorithms more fundamental. So if I am, I'm hiring somebody for a data scientist position, for example, it's or a data engineer position, I would really expect them to understand the fundamentals of these algorithms and be and also have the capability to apply this in a situation, which is not the standard textbook situation. Right? So I'm this this, I mean, the goal of this, you know, the course and say, is to make you ready for that, right. So that that is what I would say,
yeah, we want to add something that I left behind a little bit. So what Ron said about the term data scientists being abused, right, this is very, very,
on the on the docket. So at least when this whole hype started a couple of years back, I mean, if you knew how to run macros on the spreadsheet, you are considered a data scientist, right? So there are people were actually, you know, happy to give you a job and wait for you to produce some magic and things like that. But now that the field has evolved, I mean, looking at it from a slightly larger perspective, the industry has evolved. Now people kind of understand what is it that you need to know to have somebody who is valuable as a data scientist in the long run, right? And the fact that just being a tool useful, this qualifies you for data scientists is kind of getting stretched out, right? So that's no longer the case. Right? So people are really asking the Lord really think No, but you can feel behind the tool and know or understand what is happening, the tools are changing. tools are developing at a very, very rapid rate, right? So what you're good at today might not be the tool of choice for next year. Right? So so people really, really, really need to understand why are you doing something a particular way, we are not going to tell you how to develop new algorithms. That is not the goal of this course. I'm not here to make your research, right. I mean, I'm doing that with the guys who are going through my PhD program and my research programs. And my goal, that is to make them into researchers, the way we train them is very different. So what we are looking for, is to say, Tell you Okay, here are some of the standard algorithms that people use. And this is how you would use it, you know, what are the knobs that you have to turn? And what are the places it can break out? you fix it? Right? And when bad? Would you go for the next level of learning? Right? So these are the things that we really like pick up? Well, and what people are now looking for in the industry also, is that right? Not just, you know, do you know your data structure, I mean, people that just not just a coding challenge, right? So they ask you questions on
Can you do this? Can you do that? When will this break, and I'll give you an unconventional situation, that is a large class imbalance probably solve this problem? And things like this. See, the perfection is called the textbook example. Right? Because it never exists. So
we have to train you for the real world. And that's
absolutely and I think, yeah, both the observations on the data science thing is on the money, I'm sure of all the recipes that have been sent, I'm sure every ESOP would have had that I want to become a data scientist for this program, I can pretty much bet on it that that's something that we will see. So that's excellent. So we look at some of the questions you know that the audience has been sending in and while we do that, professors I go through the set of questions you know, I'll answer them if I can and then you know, questions there are certain specific questions where you know, your answers will be much valuable than fine. So we'll do that at the same time what I'm doing is I'm also sharing in case you need more information about you know, getting into the program etc, the seats that are there, you can contact her because the number is given there or you can email us at IIT madras.edu, semi talentsprint.com. We will be happy to help you. One other thing that I would like to say is that as a part of this program, all communication will be through talentsprint you will not be getting communication from IIT Madras. So that's something that we would like to say this is a program your certificate
We'll be from the Robert Bosch center of data science and artificial intelligence and which is a part of the IIT Madras. But these are the and you will be taught obviously by the faculty who are all from IIT Madras except for the bridge module.
There will be no communication that comes from IIT Madras for you neither will be placements part of the program and I'm going to talk about that as well will be covered with IIT Madras will have nothing to do with your placements, etc. Neither will you be an alumni of IIT Madras, so to speak, professors you can, you know, I hope I what I'm conveying is correct. Yeah.
Trent. Thank you. So let's look at this. This is a question from aka. And I think Professor Ron can answer this probably is it possible to get government jobs in the data science fields? Professor Rajkumar mentioned about trying analytics? web time analytics is not only something to do with the government, but you're a professor, or maybe you want to help us with an answer. I think
I work a lot with the government, more than we talked about how much I work in the industry. But I've actually worked a lot. I mean, I participate in a lot of programs at an API org and a bunch of other government bodies. And I know that the government is also very hungry for people with data science expertise. In fact, with nithi, we are working on this program where more and more of the government data is going to become available, not just available online, but available in a form in which you can do analytics, no
matter whether you can run data science algorithms on the government data, it's going to become available on the web in a variable format. so and so and they are very keen on, you know, more and more expertise in this. In fact, the Robert Bosch Center has a training program specifically for certain government departments. That's, in fact, we take Navy, we do this annual program for the naval officers. And we do a quarterly program for people from the Ministry of statistics. So there is a lot of hunger for people with data science and
AI qualification in the government. So yes, you can look for a government job. But of course, the getting into the government is different things. Jobs exist, but don't ask me how do I get?
And yeah, I mean, I can take the example I mean, we run our other data science programs, etc. This is the first time that you're doing for this with IIT Madras, but from the other programs, which we run, we always get people from, you know, various government departments who are a part of the program. So that's something that the government is actually actively looking at.
This is a question The next question is something that you know, I'll take up, you know, how much package can we expect initially after the course,
this is from a rubbish, rubbish, if you really look at it, there isn't any answer, right? If somebody's saying that you will get x package etc, I would request that you take that with a big pinch of salt. A couple of reasons for that is the fact that, you know, we don't know I mean, your situation, if I both of us are sitting in the same interview, how I perform versus how you perform, you perform brilliantly, you can get probably double the salary that I will be able to get, right. So that's one. The other part is that most people, when I talk to them, everybody only talks about the package that I will get nobody focuses on the skills part of it. The package is a result of your skills and your ability to kind of showcase that skill in an interview panel, right in the interview, and after you get through the interview in your real life. And that's what the professor's were also talking about. So you should not look at look at gaining the skills be the package will follow most cases. So it's very difficult to answer the question. But yes, what we would like to do is talentsprint has a very vibrant placement engine that's there. And every month, we have up a huge demand from companies, pop companies who want people to be placed who want you know, people with talent, actually, and that's what the industry wants, they want people who can come in and deliver. So that demand is there, if you can fulfill that, you will get a very competitive rate of let's not put it you will get something which is more than competitive in the market. Most cases what people focus on is how much is the package now even if I were to get a very fancy package, there is no guarantee nobody can guarantee on this earth that you will get the same thing. So my focus would mostly be on building the skills The package is a function that will follow even if you were to get a degree and even I tell this is before I joined talentsprint I used to head admissions for Indian School of Business. One of the best B schools in the world. It's the same thing people would always focus on you know, ask me whether I do this the simple answer
service that you can probably pass out from the best university in the world. But if you don't have the skills you will you may end up a job you will not be able to hold on to it. So the response to all the questions when it comes to you know, what is the package what is there I think there are a few questions that have come in would be this considered on the skills if you have that, you know, package is not going to be a constraint, we have so many cases I think you can even come up, if you've seen the talentsprint page, if you follow us on LinkedIn, you will look at the kind of offers that people get so that's just that's not an advertisement of the fact that you will get this if you do the program, a lot of what you get depends on you know, what you do, how much you learn etc, etc. But the system at the backend these the placement engine is well geared up to support people who have the requisite skills.
We go on to the next question.
This is from Partha Veera, will we get the basics of the tools we are going to use like Python, taboo r etc, etc. Professors perhaps you may want to answer this. Sure, so, so the bridge module that will happen that talentsprint will
will take care of will cover a bit of programming, right? So for people who are not so much equipped with programming, probably things like point and maybe Google collab and things like that, perhaps will be covered in that right. So if that is what the question is about, then yes, so you will understand the basics of programming, but there's not a programming course right. So. So it is only to bring you to a level such that so that you can follow the rest of the course Yeah,
just adding to what Professor Irwin said, the bridge module will cover two aspects. One is the basic mathematical concepts that you need to do because there's something that you know, you will need to understand to be able to get value out of the program, as well as Python and the other tools that you will need it will be a refresher on these. Most of the people whoever is coming in will be knowing some kind of programming language, etc. And we kind of feel Python is easy to learn that way. So you'll be what we'll be doing is we will be covering the Buna, we'll be spending a couple of weeks trying to give you all of that and even before the the actual live sessions begin, for the bridge module, you will have two weeks of self reading material which is there which will cover all of these aspects where you will be able to brush up and then coming to the live sessions. And then the modules one to six, that's going to be shown here in the presentation, they will start from the faculty of IIT Madras. And as a part of I think one of the things that he's trying to ask is, you know, the kind of tools that we are going to be using during the program itself. So I think that's what you know, is the is the question import of the question if I've got a trade processor
Yeah. So for example, so there are several so when you say tools, right? So, there are a lot of these you know, packages, for instance deep learning based packages, which are, which people use a lot. So some of these you will be introduced in during the deep learning module itself, right? So, but it's not going to be exhaustive in any sense, right? So we are going to perhaps talk about the important packages that people typically use to run these deep learning algorithms while you'll also get a chance to play around with these packages. But there are like a laundry list of packages available out there. It's when we It is hard to cover all of them, but then enough will be covered. So that you will be equipped to you know, play around and run these algorithms yourselves.
Absolutely. So, let's get on to the next question. This is again question from Adi, do you know do we cover only fundamentals or will we cover stuff which is a little more than fundamentals intermediate levels though, I don't know what what is the definition of intermediate level here, but maybe professors one of you may want to take a crack at this
okay. So, when you say fundamentals, so, there are so again
the module two or module two, which was the fundamentals, so rather module three, which is a fundamental algorithms in machine learning. So, they are classical machine learning, welcome straight. So, whereas if you look at module four, module four, which is the deep learning foundations and algorithms, so, most of these algorithms, in some sense, a lot of these algorithms are being developed as we speak, right? So, there are, there are new architectures in deep learning that come up. I mean, you have around 4000 5000 papers every year being published in deep learning, right. So, the goal is to, you know, cover the state of the art algorithms. We
People use today. Right? So in that sense, these will be state of the art, if that's what you mean by intermediate level. Yes. So we will cover cutting edge deep learning algorithms.
Okay, right. Thank you. So let's go on to the next question. This is okay about placements again. Okay, one other clarification that I would like to give is placements will be offered to people who have up to five years of work experience people above that will not be a part of the placement engine, the direct placement engine, that is where, though we have you also have a very, you know, wide set of view will be able to connect with all of our alumni across all of our partner institutions, there are lots of offers, you know, and job offers that are put up as a part of that alumni portal, if you have more than, you know, more than five or six students, you will be able to be a part of that many times what has happened is people have also collaborated between class you know, in the class itself, people have collaborated, we've had so many cases of startups forming, we've had so many cases of people hiring one of their classmates etc, in a position in their company, all of that keeps happening, I'm hoping that all of that will continue to happen. But the placement support per se, which we are talking about in the program on the program page, if you look at it that will be available to people with one to five years experience of neuro zero to five years of experience so to speak up to five years, you will be a part of that other let you look, you'll be able to be a part of some of the prep sessions that we do. There are so many other industry Connect sessions that keep happening, you know, every month they happen, you will be able to you know, join all of those sessions, but placements will be restricted to people with five years experience. Rakesh, if, okay, if you're not able to complete the course of the given period of time, I'm guessing, you know, most people will be able to, you know, do that, if you're not able to do it, you can follow, there are various ttv sessions will all be live sessions, but you will have, you know, the videos that you will be able to refer to etc, 12 months is a long enough period view, most of our programs are six months long, so I don't think you should have too much of a trouble in completing the program.
Okay, Avi has asking, you know, first batch what is the what are the aspects that have been taken to ensure that the program is successful, I believe we covered some of that, you know, the very fact that, you know, we will, we will, you know, look at the class, and we will try and get the faculty mentioned, you know, we will try and get people with the relevant expertise from the industry to come in, you know, join in, again, are various other things that happened as a part of the program, which, you know, some things like office, you know, we could have office hours with the faculty, we have mentors, which who are available, who could help you out with that, as well. So a lot of these aspects V, the entire bridge module itself, you know, was put up because we want people to at least have the basics in place before they come into the program. So,
and, you know, let's, I mean, I'm completely discount not discounting the fact that, you know, both the professors, and everybody involved in the program have had many years of experience of teaching students, so you can be rest assured that, you know, the your success is something which is paramount in this program, and we'll take all efforts to make that happen. Professors if you would want to add to anything that I said.
I mean, I guess you cover pretty well, so I don't have anything specific to add. Sure,
just to, you know, kind of keep teaching new new courses almost on a regular basis right here. This is something that, you know, we are used to thinking about all the time, but we will, what are the bases that we will cover and so on so forth. And so
I just echo what Arthur said, we have your back.
Absolutely, thank you. So again, this is a question from Karthik, you know, what percentage of the class will be Curie and what percentage will be practical deployment? A very pointed question Professor probably you may want to help us with an answer. So, again, if you looked at those six modules that were shown earlier, right, so, two are kind for dedicatedly for real world use case and so on. So I if I'm not wrong out of the room, 200 hours of the entire course, around 75 to 80 hours will be covered in these two modules, which is like more than 1/3 right. So, so you can think of it as Yeah, so that is that is the amount of time we'll spend only talking about
Use Cases research challenges and so on people from industry and also people, the faculty here will talk about that, of course, you cannot make it like 6040 right? So because the foundation is is equally important, right? So otherwise, I mean, you cannot build the palace without the foundation. So you that that is that is also important, so we'll spend enough time that So, I would say something like 6040 where 60 is, is algorithmic aspects and 40 is like real world use cases. Absolutely. Thank you. We in the interest of time we'll go to the next question. Okay, I am not covering questions like you know, your mechatronics pressure or something very close to you know, your profile that you do, you can you can pull up pericarp or you can write to us, we'll be happy to do a one on one counseling in the interest of time. So, okay, this is a very favorite question I was expecting why at the start debate, how many hours will it require per week? And how is it different from the online BSc degree program which is offered by IIT Madras?
Okay, so the online BSc program is the focus there is different. So it's like to first of all, it's like a PSC. Right. As the name suggests, it's the BSc level bachelors level program, it is for people. So anybody who has finished the, if I'm not wrong, attend standard or plus two can potentially update the program.
I when it was not not, not as well, great. So anybody with that
profile can actually apply for that program. And the goal there is to do it at like a very undergrad level, right. So whereas this is at a PG level kind of program, and there is no real Industry Focus in the online BSc program. So it is it? I mean, there is no, as far as I understand there is no dedicated industry use case or people from industry coming and really speaking about their challenges in the industry and so on. And something Oh, yeah, so I have both of us have been involved in forming the curriculum for online VC program as well. And I've been proudly pushing the agenda, right. So that there the bonus, it is the first degree where there's an undergraduate degree to teach things like English also, that is a program course on that, it's really a full fledged three year degree that you have to do. It's not like a weekend program where we have so much more interaction with the faculty that we have here, right, that the faculty have lectures, and then you have mentors over to your talk to the class sizes are much, much larger, right. But the goal is very different. The goal is to basically keep you with an undergraduate degree, so there's so much more that you would learn. So you would learn basic fundamentals of programming, if you're not where you're coming from at all standard background. So you learn things like data structures and algorithms, you'll learn multiple, you know, the fundamentals of databases, it's not just a machine learning program, not just the applied data science program. So the scope is very different from the online basic program. And like Aaron was saying that, yeah, because the scope is so much more fundamental teaching you literally the basics that will not have you not a lot of basic math, and which we won't go and get into that much gory detail in this program. Right? And so it's meant for a very different audience. So, that is
absolutely and in terms of odd hours over the weekend for this program, how on an average we are talking about what 10 hours in a weekend in terms of classes and other activities related to the class, can you just throw some light around that?
Yeah, so eight to 10 hours is what we are thinking over the weekend. So So if it is yeah, so these would include you know, the live lectures the perhaps the interactions, and in some cases it could be
you know, group reading sessions and so on. So depending on how the program I mean, the whole the faculty also designed the program and so on, right so
so I would say eight to 10 hours over the weekend, for 12 months, right? So that that is the expectation, right? And just to add to what the professor said now in our experience of running all of these va 10 that we're talking about are you know basic classroom activities. Now other than that, I would also request everybody to spend you know and the amount of time that you will need to spend depends on you know, how much you are prepared to etc. But I have seen in many cases we have seen that you know, people spend on an average of you know, 810 more hours depending on what they want to do to brush up the concepts etc, etc. So you know, for these next 12 months, you may have
to budget for some more time over the week as well and it totally depends on the strategy that you have of learning units I've seen some people's spend two hours in a day some people spend you know one whole Saturday morning or Saturday evening something like that to kind of go through the concepts brush up go through all of that so that's the kind of your time commitments that we are asking but then if you really look at it it's it's something that you know 1000s of other professionals are doing as a part of it, it is not something that will interfere too much with oral interview in any way with you know your work commitments etc. You also get a lot of these questions I have a lot of work commitments I can do that you know, these are programs which are designed for working professionals and the load will be kept in mind while you know why you are asked to undergo this program. One other question that we get is you know, some books that are reflective of the curriculum standard, I think, you know, Karthik is asking this question. So, professors would you want to recommend any books that people can you know refer to as a part of the program
Okay, so the okay so there are different people teaching this programming so they would have their own focus books which might be relevant to that part of the course that they are teaching overall some of the so this is from like a pure machine learning point of view right so some of the books which are which are excellent for machine learning and deep learning. Or I would say the book pattern recognition and machine learning by Christopher Bishop it's like a classic book on machine learning. There's a book on deep learning by Ian Goodfellow, which is another classic book on deep learning. So these are like fundamental books. But so what I want to stress is that this is not like a only a bookish course right so so it is much more beyond what the textbook is going to offer right so in terms of as we have been talking the making you industry ready the books are important no second thoughts about that. But then the course is much more beyond the books also.
So there's okay so there's a question from Guney two will we be using cloud platforms like ashore for deployment?
Okay, when you say deployment, what do you mean I think what what is some of the more like hands on exercises What to do?
Perhaps we'll be using something like Google collab or
something which is mostly used for most of the other programs as well. Yeah.
So we have already I've already covered questions on placements admissions application dates admissions are still open by the way you know you can reach out to harika for you know details about that we have the last few seats available you can look at that placements is something that we have covered as a part of the answers I want to pursue a master's in data science AI in agriculture Can I get an admission if you fulfill the admission criteria you can get in Believe me skip the master's degree I would probably if you want to do a Master's this will probably give you some added advantage This is not equal to a master's degree. So you know you may still need to do that if it's something that you want to pursue.
Okay. Alicia, regarding coursework will we not have any communication with the professor's Of course you will be having communication with the professors professors? Maybe you may want to answer that. So, the question is regarding the coursework also will we not have any communication with the professors? No, no, I think that question came when you mentioned that all communications have to go via talentsprint I think that was the confusion. So we are when the once the course starts you will be interacting with the professors and yeah, so. So so then there will be potential I mean dedicated office hours of the day for the instructors where you can enter you maybe you can fix an appointment and then interact with the faculty one on one where you can get some of your questions clarified. So there will be interaction right. So yes, but yeah, generally what usually happens is and let me clarify the the the cohort level announcements which are made even if professors were to make it, they usually will be routed to the LMS which again, and n emails which will come they will obviously not come from the professor's email ids directly, they will be routed to talentsprint Yeah.
okay, so the Alicia wants to also work in research in a interesting research area at Robert Bosch center, etc. Could this program help in any way? I mean,
Professors, any of you can probably answer.
absolutely anything. We might have more to say. But I think Robert Boyd center, self help. I mean, there are lots of interesting opportunities open for, you know, people who are with a bachelor's degree people with master's degrees
I will actually add it so. So I mean, like Aaron was pointing out there are a lot of opportunities that are open at the center, right? So we are not that we say that we will not be giving any special opportunities for people who are going through this program. But any of our programs at the center has a very rigorous selection process. Right? So you have to know how to write programs. So we have a programming a couple of programming rounds, and then we have a technical interview round. And then you're shortlisted for right and then you have interaction with faculty before you actually get into the any of the positions. Right. And certainly doing this program will help you go through both the programming rounds and the technical interview rounds, this
certainly prepare you for, you know, getting through those rounds. In fact, we get 1000s of applications every every year for various positions at the center. And we ended up shortlisting maybe 10 or 12 people even to intern maybe per quarter. So maybe 10 people per quarter is what we end up interviewing. So for you to get through that hotbar this person
Yeah. So maybe if you can add it, so this The goal of this course is to make you industry they need it. So which means it will also make you let's say RBC decides ready but that does not mean you have a guarantee to get into RBC, right, it will go through the process, but then it will make you it is.
So this is again, this is a question from aka, you know, we won't be actually receiving a certificate from IIT Madras professors, you may want to just talk about it. This will be from the Center for continuing education at IIT Madras. Right. So I wanted to get back to that. Because I noticed that question, I think it came up, because you said the certificate will come from the Robert Byrd center.
I think you'd like immediately after that question. So no, the certificate will come from the continuing education center of IIT Madras, right. So it's not a degree program of IIT, Madras. It is more like a continuing ed program. So the certificate will come from the continuing education and talentsprint jointly, right. And it'll also be mentioned that the program was coordinated with Robert Ford center, all of this will be mentioned on certificate, but issuing authority, quote, unquote, issuing authority will be the country Education Center at it, members and parents.
Absolutely. Thank you for the clarification. So I think we will take you know, there's a related question, what's the role of talentsprint? Well, the the platform that will be used to teach this program is something that will be provided by talentsprint talentsprint NSC talentsprint, we are a part of the National Stock Exchange is, is going to be providing that we are also responsible for doing all of the, you know, the marketing of the program, the admissions part of things, and we'll also be teaching the bridge module of the program. So that's where plus a lot of the support in terms of you know, you know, some mentors etc, are things that talentsprint will be providing the course content has been designed by Bureau, the professor's they will be teaching VB, the main portions of the profession, they will be teaching all the assessments will be conducted by them using the platform that we have. So that's that's what is there in terms of, you know, does every course on talentsprint have placement assistance by talentsprint? Depends on the program that you are doing for programs where it's available, it's clearly mentioned where it's not mentioned, it's not available, you can go to talentsprint.com and check the information for that.
And pulkovo professional collaboration with Okay, there was another question which talked about whether you will be able to connect with the professors or not, and will they be providing any support after the program is done? And I'm answering this video before the answer that usually no, but we are aware of many cases where people have collaborated with faculty after they have passed out of the program. If you have you know, if you have converging areas of interest, etc, maybe you know, some of you are enjoying getting to Robert percentage, etc, you'll be able to do that. But as a regular part of the program, you will not be able to having said that, there are lots of events that we do as a part of for our alumni network. So a lot of help is available over there. And you will also part of the very large alumni network of talentsprint which has people from you know, say FinTech to artificial intelligence, data science, machine learning, etc, etc. So if you meet something, if you post it on those forums,
See I'm sure you know people who have experienced will be happy to help you out.
Okay, I'm just going through the questions.
The best way to reach out I think the best way to reach out those kinds of questions are kind of covered.
academic journey through the course is something that we have been covering as a part of this total duration of the course it's 12 months, it's 12 months long classes are going to be starting, you know, from the platform access will be available from the next couple of days, like many people, if you are already there, I'm sure we would have already received your platform access, and classes are going to start, you know, from the first week was the first week of October live classes. So that's there the cost of the program? Well, it's it's three lakh rupees, there are scholarships available for various categories. You know, if for if you fulfill any of those categories, you have scholarships up to 30% available on that cost. It's three lakhs plus GST and then you know, whatever scholarships are available, what is the batch size being planned?
Okay, Roshan, the batch size currently that we are looking at is about 50 people right now. And these sessions deep, deep professors will be able to handle questions for a big batch size. So professors maybe you may you may want to talk a little bit about, you know, this question
the, the answer to how will we be able to if we have a large batch, etc, which is not the case this time, you know, how do we how we'll be able to handle the questions, etc, I'll probably add from the platform capability, what's available, but you know, we want you to probably talk a little bit about that, I mean, so, the typical batch size of machine learning course, even even at IIT Madras is 100 plus eight. So, so, we are used to teaching large pack sizes, so to say so, 50 is relatively small and backsides think so, so, if the question is about will I get time to interact with the faculty will I have my doubts and questions or so, I think there are two parts right. So, one is the platform itself, I think the troll might admire
the platform itself will have capabilities, which will make this process easier. And of course, we have these officers and other things, which will also help you to interact with the faculty right. So, I don't think there will be a point where you will feel that you have questions which go unanswered right. So,
yeah, so, in terms of the platform, if you look at it, these will be live classes that you will be doing so, it's just like you know, we are having an interaction we are answering questions here and the discussion, you will be able to see the you know, whiteboards whatever the professor's have as class notes will be available on the LMS you can access that at any point in time after the session is over, you will be able to there is an AI index video that will be available so if at any point in time they are talking about convolutional neural network at some point in time you will actually be able to go to the index in the video click on the CNN and you can jump to that part of the lecture directly. The platform also has what we call as a breakout room so to speak. So in case what we are doing is in a class of say 50 there are group exercises that are happening and you have five groups of 10 people each for example we can actually break up all of these five groups into separate rooms and the faculty can get into each room and you know discuss with people you guys can work separately it's it's just like being in a physical class, but from the comfort of your own home. So all of these things are available you have what is called a lounge, which is which is which is something that is always available so students can come in pure cat and that's outside of three defined classes. You can have a group discussion over there, professor was talking about, you know, office hours with the professor so that entire should you have their availability will be published every month. You can book an office hours, which will be a one on one or group to one session with the professors get your questions, doubts, clarified, there are discussion forums that are there where you can, you know, post questions, the cohort can help you, etc. So these are all things which are possible, which the platform suggests supports very natively, and it's been a very seamless experience, just to, you know, give you some more comfort. The same platform is used being used
By I am Calcutta to run their main MBA program. So the entire two year MBA program in the last two years or the one and a half years since the pandemic has started, has been running on this platform. So all courses which is which is couple of few 1000 students are almost close to 1000 students are doing this, there's been no shoe that we have even IIT, you know, IIT Jammu has used the same platform which will be used to deliver this to deliver their engineering programs. So I hope that will clarify
the capabilities of the platform and and your ability to gain from that
in the DL module. Okay, sorry, I think this crossed the time, I didn't realize that my apologies for that professors. We can wrap it up in the next four minutes, five minutes if that's okay.
So sorry about that. I didn't realize I passed by. So in the DL module, we will we be implementing the algorithms from scratch after reading the related research paper? Or will we be using implemented packages? This is a question from goodness.
Okay, so. So to implement the algorithm from scratch, typically in a deal framework, right? So, so you would need humongous amounts of data, right? So there are often these pre trained models which are available, which you use, and to develop your own your own algorithms for your own problems of interest. Right. So it's not.
So it is not even advisable to do in detail everything from scratch. So but then you would understand how to build things from scratch. Right? So but then for the purposes of trying to implement it for for a particular problem of interest, you would use a lot of available packages also. Right? So you will, you'll be, of course, writing your own code, but then we'll also use some of these existing packages, it will be a mix of both.
Absolutely. Thank you. So we'll take up the last couple of questions. Okay, there's a question on campus visit Well, there are campus visits which are planned as a part of the program, but that is subject to COVID, etc, maybe professor, you may want to Giro,
take a crack at that.
It doesn't. So, in fact, our our campus is not even welcoming our regular students back yet. Right? So. So there are a lot of quarantine constraints, the students want to come back, they have to do a 14 day quarantine before they can resume normal activity and things like that. So hopefully, somewhere around as
the program progresses, restrictions will lease up. And so subject to the situation COVID conditions and the government directives, because we have a central government organization, right? So we have to follow everything the government says. And so it depends how much very much on how the condition works. Right now, I can't guarantee when we will, yes, that I really would love to see students back on campus, but
we don't know when that's gonna happen.
Absolutely. So I think we've answered most of the question, funeral networking group, etc. When the world is very network place, I'm sure you know, if you want to connect with people, I don't want to put in ideas here. But in most of the programs, I am aware of slack groups, etc, etc. I'm aware of WhatsApp groups that are there. These are settings which are led by students, I'm sure all of you will find that as a part of the group. If the professors want to be a part of that I'm aware of many programs where they are, I am aware of many programs that are not so it's all left to students, how would you want to interact with the professor's I'm sure they would love to do that. So that's all I think we have overshot our time by 20 minutes. Thank you so much for spending time over here. Um, it was a lot of information that we got from you from in terms of the program what we are planning to do. And I really thank both of you both Professor rude as the professor Ravi, for you know, spending this time and you know, talking to some of the students I you know, I see many of their names already there. And many people I'm sure are interested in case you want to join this program. We have some seats available, but you will have to work fast. Dates are closing very soon. Contact Erica, I'm sure she'll be very happy to help you with the process. And thank you again, I hope all of you stay safe. We look forward to seeing most of you in the class, which is or in the sessions which are coming up very soon. Thank you both professors for spending time with us. I know both of you are very busy. We really appreciate the time that you spent. And it was a great session. Thanks. Thank you. It's really looking forward to this
Senator program. Thank you so much. All the best. Have a good night.
Watch the entire interview here https://www.youtube.com/watch?v=MEZGv1y_2ts
Note: This video transcript is generated by AI. Therefore, it may not be 100% accurate.