Introduction to Machine Learning (Spring 2020)
Announcements
Assignment 1 is released.
Lectures
(January 22, 2020) Lecture 1 : Introduction and Formal Model of Learning (cf. [R1], Chapter 1). Paper by Leo Breiman on the two cultures can be found here.
(January 27, 2020) Lecture 2 : PAC Learnability and Agnostic PAC Learnability (cf. [R1], Chapter 2)
(January 29, 2020) Lecture 3 : Learning via Uniform Convergence (cf. [R1], Chapter 3)
(February 3, 2020) Lecture 4 : NoFree Lunch Theorem and BiasComplexity Tradeoff (cf. [R1], Theorem 5.1, Corollary 5.2)
(February 5, 2020) Lecture 5 : VCDimension Theory (cf. [R1], Chapter 6)
(February 10, 2020) Lecture 6 : The Fundamental Theorem of PAC Learning (cf. [R1], Theorem 6.7, Lemma 6.10, Theorem 6.11)
(February 12, 2020) Lecture 7 : Convexity and Learning (cf. [R1], Chapter 12)
Final Project
Term Paper, due by Wednesday, April 29, 2020, 2:00 pm
Presentations on Wednesday, April 29, 2020
Reference Books
[R1] Understanding Machine Learning : From Theory to Algorithms, Shai ShalevShwartz and Shai BenDavid
[R2] Pattern Recognition and Machine Learning, Christopher Bishop
[R3] Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, and Jerome Friedman
[R4] Introduction to Statistical Learning, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tobshirani
[R5] A Brief Introduction to Machine Learning for Engineers, Osvaldo Simeone
General Information
Instructor : Himanshu Asnani
Venue : A201
Class Timings : Mondays, 4:00 pm  5:30 pm and Wednesdays, 2:00 pm  3:30 pm
Grading : Class Participation (10%), Homeworks (60%), Final Project (30%)
Assignments : We will be using Jupyter Notebook for the programming aspect of the course. Programming language will be Python and the installation guide can be located here.
Tentative Schedule
Module I [Theoretical Foundations (8 lectures)] PAC Learning, BiasComplexity Tradeoff, VC Theory, Generalization, Gradient Descent
Module II [Supervised Learning (8 lectures)] Regression, Classification, Support Vector Machines, Decision Trees, Bagging, Boosting
Module III [Unsupervised Learning (8 lectures)] Clustering, Dimensionality Reduction, Generative Models
Module IV [Deep Learning (3 lectures)] Neural Networks, Backpropagation, Generative Adversarial Networks (GANs)
