Theory: Linear Algebra, Analytic Geometry, Matrix decompositions, Vector Calculus, Probability and Distributions, Continuous Optimization. Refer Chapters 1-7 from the MML book.
Programming: Python: Source, Pytorch: Source.
Software and Packages: Anaconda, Jupyter notebook
1. ML - Tom Mitchel. Machine Learning.
2. PRML - Chris Bishop. Pattern Recognition and Machine Learning
3. PC - Richard O. Duda, Peter E. Hart, David G. Stork Pattern Classification.
4. PML - Kevin Murphy. Probabilistic machine learning
5. ISL - Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani. An Introduction to Statistical Learning
6. CML - Jonathan Shewchuk. Concise Machine Learning
7. ESL - Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
| Week | Topic | Reading | Additional and Assignments |
|---|---|---|---|
| 1 | Introduction, Mathematical Preliminaries, Linear classfiers, Perceptron | Section 4.1.7 from PRML. | |
| 2 | Optimization Basics, SVM | Section 7.1 from PRML. | |
| 3 | SVM Continued | Section 7.1 from PRML. Read ESL, Section 12.2 up to and including the first paragraph of 12.2.1. | |
| 4 | Bayesian Decision Theory | Chapter 1,2,4 from PRML. Section 2.1 to 2.7 of PC. Chapter 6 and 7 from CML. | |
| 5 | MLE/MAP, Naive Bayes | ||
| 6 | Linear Regression, Logisitic regression | ||
| 7 | Decision Trees | ||
| 8 | Optimizations, Bias-Bariance Tradeoff, Model Selection | ||
| 19-21 | Dimensionality reduction, PCA, LDA | ||
| 22-25 | Perceptron, SVM, Kernels | ||
| 26-32 | Neural Networks | ||
| 33-35 | Unsupervised methods: Clustering | ||
| 36-37 | Ensemble Methods |
TA: Zafeer, Ayush, Srinath ({223CS3141, 223CS3159, 223CS3363}@nitrkl.ac.in)
Office Hour: Tuesday 11AM-12AM.