This course will introduce students to various theoretical and algorithmic aspects of machine learning. Prior knowledge of Probability-Statistics, Linear Algebra and calculus will be required. The course also includes a lab component.
Instructor: Dr. Divya Padmanabhan TAs: TBD
Lecture Timings: Wed 11-12, Thurs 9-10, Fri 9-10 Venue: LT1, Admin building, IIT Goa
Lab Timings: Monday 3 - 6 pm. Venue: CC lab
The following references will be directly relevant for the course.
Pattern Recognition course by Prof PS Sastry, IISc Bangalore. Link to YouTube playlist
Learning from Data course by Prof. Yaser S. Abu Mostafa, CalTech.
Textbook: Pattern Recognition and Machine Learning by Christopher Bishop
Textbook: Introduction to Machine Learning by Ethem Alpaydin
Textbook: Pattern Classification by Richard O. Duda, Peter E. Hart and David G. Stork
Caution: Being a very popular subject there is a lot of content on the web, however not all resources provide accurate information. Hence please don't blindly believe resources that you find online.
Bayes Classifier, Estimator properties, Maximum Likelihood Estimation, Bayesian Estimation
KMeans clustering, Expectation Maximization Algorithm and theoretical foundations
Linear Models: Perceptron, Logistic Regression, Regression based classifier, linear regression
Statistical Learning theory: PAC Learning, VC dimension, Bias Variance Tradeoff
Feed forward Neural networks, Convolutional Neural Networks
Theory 75% [comprising the following components: quizzes (15%), a mid-term exam (25%), a final exam (25%) , class participation (10%)], Lab 25%
Collaboration is strictly NOT permitted for any of the quizzes and exams. Please approach me if you have any questions.
Lectures