Machine Learning - CS419 Reading Material

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This is a part of our Machine Learning article. Prof. Preethi Jyothi conducted an "Introduction to Machine Learning" course at IIT Bombay in Spring 2018. In this article, we have compiled all the reading material in this course. Also, have a look at the original course webpage.

Basics of Probability for ML

       Reading:, Sections 3.1-3.8, 3.10, 3.11, 3.13

Linear Regression (incl. Regularization)

       Reading:, Sections 2 & 3
       Reading:, Sections 1.2, 1.3 and 2
       Reading:, Chapter 3.4 (everything before Eqn 3.45)

Naive Bayes & Logistic Regression Classifiers

       Reading:, Entire chapter

Decision Trees

       Reading:, Entire chapter

Perceptron Algorithm


Basics of Statistical Learning Theory / Bias-Variance Tradeoff

       Reading:, Section 11.1 on VC dimension

MLE vs MAP Estimation

       Reading:, Slides 1 to 35

SVMs + Kernel Methods

       Reading:, Technical details about QP/lagrangian optimization are not important
       Reading:, Slides 1 to 32

k-Means Clustering + Gaussian Mixture Models

       Reading: Chapter 9 from the book "Pattern Recognition and Machine Learning" by Chris Bishop, Entire chapter

Principle Component Analysis (PCA)

       Reading:, Sections I to V

Feedforward Neural Networks + Backpropagation


Ensemble Learning (Boosting / Bagging)

       Reading: Chapter 14 from the book "Pattern Recognition and Machine Learning" by Chris Bishop, Sections 14.3, 14.3.1
       Reading:, Slides 1 to 27

Fundamentals of hidden Markov Models