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 : No-Free Lunch Theorem and Bias-Complexity Tradeoff (cf. [R1], Theorem 5.1, Corollary 5.2)

    • (February 5, 2020) Lecture 5 : VC-Dimension 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)

  • Assignments

  • Final Project

    • Term Paper, due by Wednesday, April 29, 2020, 2:00 pm

    • Presentations on Wednesday, April 29, 2020

  • General Information

    • Instructor : Himanshu Asnani

    • Venue : A-201

    • 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, Bias-Complexity Trade-off, 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)