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BSCS2004 - Machine Learning Foundations
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BSCS2004 - Machine Learning Foundations
636 words
3 min read
| Field | Value |
|---|---|
| Course Code | BSCS2004 |
| Level | Diploma Level Course |
| Credits | 4 |
| Type | Data Science |
| Pre-requisites | None |
| Videos | YouTube Playlist |
📖 Description
This course lays the groundwork for the upcoming ML courses by covering various fundamentals that do not necessarily fall under Machine Learning but are quite necessary for a comprehensive understanding of Machine Learning.
🗓️ Weekly Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to machine learning |
| Week 2 | Calculus |
| Week 3 | Linear Algebra - Least Squares Regression |
| Week 4 | Linear Algebra - Eigenvalues and eigenvectors |
| Week 5 | Linear Algebra - Symmetric matrices |
| Week 6 | Linear Algebra - Singular value decomposition, Principal Component Analysis in Image Processing |
| Week 7 | Unconstrained Optimisation |
| Week 8 | Convex sets, functions, and optimisation problems |
| Week 9 | Constrained Optimisation and Lagrange Multipliers. Logistic regression as an optimization problem |
| Week 10 | Examples of probabilistic models in machine learning problems |
| Week 11 | Exponential Family of distributions |
| Week 12 | Parameter estimation. Expectation Maximization. |
📝 About the Instructors
Harish Guruprasad Ramaswamy
Assistant Professor,
Department of Computer Sciences & Engineering,
IIT Madras
I am currently an assistant professor at the computer science and engineering (CSE) department of IIT Madras. My primary areas of interest are in machine learning, statistical learning theory and optimisation. I was previously a research scientist at IBM research labs and a post-doc at University of Michigan. I completed my PhD at the Computer Science and Automation (CSA) department of the Indian Institute of Science (IISc), Bangalore advised by Prof. Shivani Agarwal. I have been fortunate to work with Profs. Ambuj Tewari and Clayton Scott during my PhD and postdoc. Earlier, I finished my M.E. under the supervision of Prof. Chiranjib Bhattacharyya.
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Arun Rajkumar
Assistant Professor,
Department of Data Science and AI,
IIT Madras
I am currently an Assistant Professor at the Data Science and AI department of IIT Madras. Prior to joining IIT Madras, I was a research scientist at the Xerox Research Center (now Conduent Labs), Bangalore for three years. I earned my Ph.D from the Indian Institute of Science where I worked on 'Ranking from Pairwise Comparisons'. My research interests are in the areas of Machine learning, statistical learning theory with applications to education and healthcare.
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Other courses by the same instructor:
BSCS2007 -
Machine Learning Techniques
and
BSDA5007 -
Special topics in Machine Learning (Reinforcement Learning)
Prashanth LA
Assistant Professor,
Department of Computer Sciences & Engineering,
IIT Madras
Prashanth L.A. is an Assistant Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Madras. Prior to this, he was a postdoctoral researcher at the Institute for Systems Research, University of Maryland - College Park from 2015 to 2017 and at INRIA Lille - Team SequeL from 2012 to 2014. From 2002 to 2009, he was with Texas Instruments (India) Pvt Ltd, Bangalore, India.
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He received his Masters and Ph.D degrees in Computer Science and Automation from Indian Institute of Science, in 2008 and 2013, respectively. He was awarded the third prize for his Ph.D. dissertation, by the IEEE Intelligent Transportation Systems Society (ITSS). He is the coauthor of a book entitled `Stochastic Recursive Algorithms for Optimization: Simultaneous Perturbation Methods', published by Springer in 2013. His research interests are in reinforcement learning, simulation optimization and multi-armed bandits, with applications in transportation systems, wireless networks and recommendation systems.
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