Indian Institute of Technology Madras (IIT Madras) is globally recognized for excellence in technical education, basic and applied research, innovation, entrepreneurship and industrial consultancy. Founded in 1959 with technical and financial assistance from the former government of West Germany, IIT Madras has been the top-ranked engineering institute in India for four consecutive years as well as the ‘Best Educational Institution’ in Overall Category in the NIRF Rankings by the Ministry of Human Resource Development. For more information visit www.iitm.ac.in
The PG Level Advanced Certification Programme in Applied Data Science and Machine Learning course will be delivered by the Robert Bosch Centre for Data Science and AI (RBCDSAI), one of India's pre-eminent interdisciplinary research centres for Data Science and AI with the largest network analytics, deep reinforcement learning, and the most active natural language processing and deep learning groups. The Centre was started to expand AI adoption in engineering applications and leverage the available expertise on network systems modelling across various Institute departments. The programme Certification will be awarded by the CODE (Center for Outreach and Digital Education) , IIT Madras that is committed to helping build national capabilities in science, technology, humanities, management, education and research.
The 12-month online Applied Data Science and Machine Learning Course , offered in partnership with the Robert Bosch Centre for Data Science and AI (RBCDSAI) at IIT Madras, is a cutting-edge course designed to enable learners to build deep tech capabilities and make data-driven business decisions. With the massive amount of data generated daily from millions of devices, Applied Data Science has become a crucial field in today's world. The program offers a unique learning experience that combines masterclass lectures, hands-on labs, hackathons, workshops, industry interactions, and a campus visit to fast-track learning.
The IIT Madras Data Science course covers various topics such as data preprocessing, machine learning algorithms, deep learning models, and natural language processing, among others, to prepare learners to handle complex data sets and provide data-driven solutions. The curriculum is designed to meet industry standards and includes real-world case studies, ensuring learners have hands-on experience working with industry-relevant tools and technologies. The course also enables learners to acquire soft skills such as communication, teamwork, and leadership, which are essential for success in the workplace.
Participants of the course benefit from the expertise of leading faculty members and industry experts, who provide personalized guidance and mentorship throughout the course. Upon completion of this IIT Madras Data Science course, learners receive a PG Level Advanced Certification in Applied Data Science and Machine Learning from IIT Madras, which enhances their career prospects and enables them to become sought-after professionals in the data science and machine learning field.
IITM Campus Visit
Linear equations and solutions Matrices and their Properties; Eigenvalues and eigenvectors; Matrix Factorizations; Inner products; Distance measures; Projections; Notion of hyperplanes; halfplanes.
Probability theory and axioms; Random variables; Probability distributions and density functions ;Expectations and moments; Covariance and correlation; Statistics and sampling distributions; Hypothesis testing of means, proportions, variances and correlations; Confidence intervals; Correlation functions; Parameter estimation – MLE and Bayesian methods
Unconstrained optimization; Necessary and Sufficiency conditions for optima; Gradient descent methods; constrained optimization, KKTConditions; Introduction to least squares optimization;
1. Use cases from the healthcare domain where NLP is applied
2. Models such as Bi-LSTM-CRF, CAML, HAN, ResNexT.
3. Public domain datasets - MIMIC-III.
Introduction to big data in biology
Levels of omics data, basic information flow in biology
Importance of Networks in Biology: Overview
Introduction to Network Science
Learning from Network structure: Predicting essential genes
Learning on Networks: Community detection to identify disease genes - Learning using Networks: Graph mining for predicting biosynthesis routes - Omic data analysis: Predicting mutations and genes that drive cancer
1. Problem Statement : Four case studies will be demonstrated. CS1: Choice of mode CS2: Travel time estimation CS3: Accident hot spot analysis CS4: Accident severity modelling
2. Model(s) intended to demonstrate : Logistics regression, Support vector regression, k-means clustering and random forest
3. Dataset to be used during the demo
4. Dataset for the mini project
1. Levels of omics data, basic information flow in biology
2. Genomics, Transcriptomics, Epigenomics, Proteomics and Multi omics - Identification human disease genes using genomics
3. Application of transcriptomics for identifying disease mechanisms
4. Clinical data - kinds of clinical data Garbhini dataset - a clinical data case study
a. Artificial Neuron
b. Multilayer Perceptron
c. Universal Approximation Theorem
d. Backpropagation in MLPs
e. Backprop on general graphs
a. Gradient Descent and its variants
b. Momentum, Adam, etc.
c. Batch Normalization
a. Introduction
b. CNN Operations
c. CNN Training
d. Illustrative Example (“Hello World”) - MNIST digit classification e. Image Recognition-SoTA model(s)
f. Object detection/localization - SoTA model(s)
g. Semantic segmentation -SoTA model(s)
a. Smart Cities
b. Industry Use case 1
c. Climate Science
d. Manufacturing
e. Bio-informatics
Industry Use case 2
The programme is designed by a distinguished faculty group bearing academic accreditation from premier institutions around the world.
Ph.D., Computer Science and Automation, IISc
Machine Learning, Rank Aggregation, Statistical Learning, Sequential Decision Making
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Ph.D., Computer Science, University of Massachusetts, Amherst, USA
Reinforcement Learning, Geometric Deep Learning, Data Mining
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Ph.D., Engineering Systems, MIT, Cambridge, MA, USA
Experimentation, Applied Statistics and Machine Learning
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Ph.D., Computer, Communication and Information Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Machine Learning for Biological and Engineering Networks, Safe Reinforcement Learning
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Ph. D., Systems Biology, IISc
Biological Networks, Computational Systems Biology, Genomics and Computational Biology
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Ph.D., Transportation Engineering, Rensselaer Polytechnic Institute, NY, USA
Dynamic Traffic Assignment, Transportation Network Modelling, Intelligent Transportation Systems, Pedestrian Safety
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**Dates will be decided keeping the safety of participants in mind. Fees will be based on actuals.
Program Fee ₹2,50,000 (18% GST as applicable)
Application Fee ₹2,000
Special Pricing for
Corporates
Campus visit fee to be borne by participants. Will be based on actuals.
Fees paid are non-refundable and non-transferable.
Modes of payment available
Internet Banking
Credit/Debit Card
UPI Payments12 Month 0% Interest Scheme / Interest-Based Schemes
EMI as low as ₹8,358/Month
Loan Partners
It is well known that Data Science helps businesses extract actionable insights from massive sets of data. Data Science and the technologies empowering it, like AI and Machine Learning, have become central to every business strategy today.
However, Applied Data Science takes the game many notches higher. It broadens the scope of data science to include
Applied Data Science becomes important in the backdrop of the fact that the global data created per day is likely to reach 463 Billion GB/Per Day by 2025 from 44 Billion GB/Per Day in 2016 according to IDC. Such massive data cannot be made sense of by traditional algorithms.
This is where Machine Intelligence (MI) can add immense value to business in conjunction with Applied Data Science.
Machine Intelligence as a higher evolution of machine learning - a stepping stone to true AI.
According to LinkedIn, Applied Data Science and Machine Learning offers exciting job opportunities for professionals with expertise in these fields.
The IIT Madras Advantage
Cutting-edge Applied Learning
The TalentSprint Advantage
The IIT Madras data science and machine learning course will be delivered in an interactive online format, retaining effectiveness while maintaining safety. The format uniquely combines the benefits of an in-class programme with the flexibility and safety of online learning.
The programme will be delivered on TalentSprint's patent-pending iPearl.ai, a leading digital learning platform of choice used for programs delivered by the likes of Google, IIM Calcutta, IIT Hyderabad, IISc, and IIT Kanpur, to name a few.
Machine Learning, Rank Aggregation, Statistical Learning, Sequential Decision Making
Assistant Professor at the Dept. of Computer Science & Engineering and core member of the Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras. Before joining IIT Madras, he worked as a research scientist at the Xerox Research Center (now Conduent Labs), Bangalore. His research interests include Machine Learning, Rank Aggregation, Statistical Learning and Sequential Decision Making.
Experimentation, Applied Statistics and Machine Learning
Faculty member in the Department of Management Studies, core member of the Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), and Co-Head of the American Express Lab for Data Analytics, Risk and Technology (DART) at IIT Madras. Before joining IIT Madras, Nandan worked as a quantitative researcher for five years at a high-frequency algorithmic trading firm in New York. His research and work experience focuses on applications of experimentation, machine learning and the abstraction of data to models and algorithms. This spans data and problems across different domains, including but not limited to finance, urban mobility, digital platforms, civic services, and criminology.
Machine Learning for Biological and Engineering Networks, Safe Reinforcement Learning
Assistant Professor at the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, coordinator of Network Systems Learning, Control and Evolution Group, core faculty member at Robert Bosch Centre for Data Science and Artificial Intelligence and Initiative for Biological Systems Engineering, IIT Madras. Nirav’s research interests include first-principle and machine learning approaches for modeling and control of biological networks from multi-sensor and multi-scale data, safe learning and control of man-made networks, and bioprocess data analysis.
Reinforcement Learning, Geometric Deep Learning, Data Mining
Head of the Robert Bosch Centre for Data Science & Artificial Intelligence (RBCDSAI) and Mindtree Faculty Fellow and Professor in the Department of Computer Science and Engineering at IIT Madras. He has published nearly 100 papers in premier journals and conferences such as ICML, AAAI, IJCAI, ICDM, ICLR, NeurIPS, UAI, ISMB, and AAMAS. He has also co-authored the chapter on Reinforcement Learning in the Handbook of Neural Computation published by Oxford University Press. His work with students has won multiple best paper awards, the most recent being a best-paper runner-up at AAMAS 2020. His current research interests are centred around geometric deep learning and reinforcement learning.
Biological Networks, Computational Systems Biology, Genomics and Computational Biology
Associate Professor at the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, coordinator for the Initiative for Biological Systems Engineering (IBSE) and core faculty member at RBCDSAI, IIT Madras. Karthik is also the co-founder of qBiome Research Private Limited, an IIT Madras incubated startup offering customised cellular, metabolic and process engineering solutions for the biotech, pharma and chemical industry. His group works on scalable algorithms to study biological networks, and machine learning using high-dimensional multi-omic data, with applications in metabolic engineering and disease biology.
Dynamic Traffic Assignment, Transportation Network Modelling, Intelligent Transportation Systems, Pedestrian Safety
Associate Professor in the Transportation Engineering Division, Department of Civil Engineering and core faculty member in Robert Bosch Center for Data Science and AI, IIT Madras. His overarching goal is to develop technological and management solutions for a safe and sustainable transportation system. His primary research interests are in network modeling, dynamic traffic simulation and assignment, Intelligent Transport Systems (ITS), vehicular emissions, urban freight, public transit systems, and road safety.
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