Topics:

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

Reference Books:
[R1] Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville
[R2] Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David
[R3] UIUC Course on "Representation Learning : Algorithms and Models" : https://courses.engr.illinois.edu/ece598pv/fa2017/home.html
[R4] Princeton Course on "Theoretical Machine Learning :  https://www.cs.princeton.edu/courses/archive/spring19/cos511/
[R5] Stanford Course on "Deep Learning" : https://cs230.stanford.edu/syllabus/
[R6] Stanford Course on "Machine Learning" : https://see.stanford.edu/Course/CS229"