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 PG Level Advanced Certification Programme in Applied Data Science and Machine Learning, 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 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 the 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
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
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 MakingKnow More
Ph.D., Computer Science, University of Massachusetts, Amherst, USA
Reinforcement Learning, Geometric Deep Learning, Data MiningKnow More
Ph.D., Engineering Systems, MIT, Cambridge, MA, USA
Experimentation, Applied Statistics and Machine LearningKnow More
Ph.D., Computer, Communication and Information Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Machine Learning for Biological and Engineering Networks, Safe Reinforcement LearningKnow More
Ph. D., Systems Biology, IISc
Biological Networks, Computational Systems Biology, Genomics and Computational BiologyKnow More
Ph.D., Transportation Engineering, Rensselaer Polytechnic Institute, NY, USA
Dynamic Traffic Assignment, Transportation Network Modelling, Intelligent Transportation Systems, Pedestrian SafetyKnow More
**Dates will be decided keeping the safety of participants in mind. Fees will be based on actuals.
The selection for the programme will be done by IIT Madras and is strictly based on the eligibility criteria and the motivation of applicants as expressed in their statement of purpose.
Class Start - April 2024
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 programme 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.