CSS.313.1 : Representation Learning (Autumn 2020)

Announcements
  • Lectures

    • (September 21, 2020) Lecture 1 : PCA, Power Method

    • (September 23, 2020 - November 2, 2020) No Lecture

    • (November 4, 2020) Lecture 2 : Probabilistic PCA, Non-linear PCA, CCA, Probabilistic CCA

    • (November 9, 2020) Lecture 3 : Non-linear CCA - I

    • (November 11, 2020) Lecture 4 : Non-linear CCA - II, Kernal-CCA

    • (November 16, 2020) Lecture 5 : MDS, Isomap, SNE, t-SNE

    • (November 18, 2020) Lecture 6 : Classical Non-parametric Inference-I

    • (November 23, 2020) No Lecture

    • (November 25, 2020) Lecture 7 : Classical Non-parametric Inference-II

    • (November 30, 2020) Institute Holiday

  • Assignments/Final Project

    • TBA

  • General Information

    • Instructor : Himanshu Asnani

    • Venue : Online

    • Class Timings : Mondays, 11:30 am - 1:00 pm and Wednesdays, 11:30 am - 1:00 pm

    • Grading : Class Participation (10%), Homeworks (60%), Final Project (30%)

    • The course CANNOT be taken for the qualifiers.

    • 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.

    • Pre-requisities : Preferable (but not absolutely compulsory) if the Machine Learning course has been taken.

    • Tentative Schedule :

      • Module I [Dimensionality Reduction] : PCA, CCA, kernel-PCA/CCA, Probabilistic PCA/CCA, Non-linear PCA/CCA, Non-linear Methods (Isomap, SNE, t-SNE)

      • Module II [Generative Models] : Generative vs Discriminative Models, Classical Non-parametric Inference, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs)

      • Module III [Miscellaneous Topics and Applications] : Landscape of Information Estimation