Information and Communication TechnologytheorySem 3
MACHINE LEARNING
ICT 4402
Syllabus
- 01Introduction to Machine Learning
- 02Mathematical Preliminaries
- 03Supervised Learning-LMS
- 04logistic regression
- 05GDA
- 06Naive Bayes
- 07SVM
- 08model selection
- 09Learning theory-bias/variance tradeoff
- 10union and Chernoff bounds
- 11VC dimensions
- 12Unsupervised learning-clustering
- 13k-means
- 14Gaussian mixture
- 15factor analysis
- 16PCA
- 17ICA
- 18Reinforcement learning-MDPs
- 19Bellman equations
- 20value and policy iteration
- 21LQR
- 22LQG
- 23Q-learning
- 24policy search
- 25POMDPs
- 26Explainability
References
- Kevin P Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
- Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, Foundations of Machine Learning, MIT Press, 2012.
- Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009.
- Christopher M.Bishop, Pattern Recognition and Machine Learning (2e), Springer, 2013.
- Richard S.Sutton and Andrew G.Barto, Reinforcement Learning, 2nd Edition, MIT Press, 2018
- Solon Barocas, Moritz Hardt and Arvind Narayanan, Fairness and Machine Learning, failml.org, 2021
Credits Structure
3Lecture
0Tutorial
0Practical
3Total