Outline

The following schedule outlines the different modules and topics under each module that we would discuss in this course over a span of 14 weeks (28 lectures). Relevant reading material and resources will be provided following each lecture.

Modules	Topics	Lecture content
Module 1	Geometric dimensionality reduction	Principal Component Analysis (PCA)
		Cannonical Correlation Analysis (CCA)
		Maximum Correlation, Discrete CCA and Kernel CCA for real-valued data,
		Alternating Conditional Expectation algorithm (ACE) and Non-linear dimensionality reduction methods


Module 2	Probabilistic dimensionality reduction	Generative models: non-parametric
		Generative models: mixtures of Gaussians ( EM algorithm and method of moments)
		Discriminative models: Mixture models (method of moments and tensor decomposition algorithms)
		Discriminative models: Mixture of Experts

Module 3	Neural networks	Neural networks (Have a look at the zoo!)
		Training of neural networks: Backpropagation algorithm and SGD
		Variational Autoencoders
		Generative Adversarial Networks

Module 4	Representation methods: NLP	Language models (KN and GT smoothing)
		Word2vec (Skipgram and CBOW) and GloVe
		Doc2vec, Sentence2vec, Skip-thought
		Polysemy, Compositionality
		Deep learning pipeline for NLP
Module 5	Representation methods: Others	Graph2Vec
		Program2Vec
		Interpretable ML