Machine Learning CS6218- NIT Rourkela

Prerequisites

• Theory: Linear Algebra, Analytic Geometry, Matrix decompositions, Vector Calculus, Probability and Distributions, Continuous Optimization. Refer Chapters 1-7 from the book `https://mml-book.github.io/book/mml-book.pdf`
• Programming: Python, Source: https://www.w3schools.com/python/default.asp,
  Pytorch\tensorflow, Source: https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf, https://github.com/gigwegbe/Learning-TensorFlow, TF book
• Software and Packages: Conda, Jupyter notebook

Books

The Majority of the course content follows from the first two references.

• Tom Mitchel. Machine Learning.
• Chris Bishop. Pattern Recognition and Machine Learning
• Richard O. Duda, Peter E. Hart, David G. Stork Pattern Classification.
• Kevin Murphy. Probabilistic machine learning

Tentative Schedule

Class	Topic	Reading	Notebooks and Assignments
1-4	Introduction, Mathematical Preliminaries
5-8	MLE/MAP, Naive Bayes
9-13	Linear Regression, Logisitic regression
14-16	Optimizations, Bias-Bariance Tradeoff, Model Selection
17-18	Decision Trees
19-21	Dimensionality reduction, PCA, LDA
22-25	Perceptron, SVM, Kernels
26-32	Neural Networks
33-35	Unsupervised methods: Clustering
36-37	Ensemble Methods