CSS.313.1 : Representation Learning (Autumn 2020)

Announcements

Course to begin on Monday, September 21, 2020, 11:30 am - 1:00 pm (online).

  • Lectures

    • TBA

  • Assignments

    • TBA

  • 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, Parametric Density Estimation, Non-parametric Models. Tensor Methods

      • Module III [Neural Networks and Deep Generative Models] : Architectures. Training, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs)

      • Module IV [Miscellaneous Topics and Applications] : Rademacher Complexity, Information Theory meets Generalization, Manifold Learning, Reinforcement Learning, Deep Bandits, Active Learning, Landscape of Information Measure Estimation