This page will provide information on what is covered in each lecture and will be updated as the class progresses.

Course slides are available on Moodle.

Date	Topics
	Overview of the course Basic course information and administrative details Supervised and unsupervised learning Learning task, instances, features, labels, reward/loss, training, testing
	Quiz on Linear Algebra (basics) Vectors, Matrices, Tensors, Basic matrix operations, Special matrix types, Eigen decomposition
	Classification and regression Overview: setup, training, test, validation dataset, overfitting. Setting up vector notations, understanding multidimensional spaces.
	Linear regression and classification Linear regression: Defining loss functions Logistic classifier: Defining logistic loss
	Numerical Optimization (basics) Review of convex function and optimization of unconstrained functions. Matrix calculus: Gradients and Hessians. < Definition and properties of convex function Unconstrained optimization algorithms: gradient descent and stochastic gradient descent
	Probability for ML (basics) Probability, axioms of probability, random variables, common distributions, means, variance and other moments, joint distributions, and conditional distributions.
	Probabilistic Generative classifiers Naive Bayes classification Generative classifiers: LDA
	Decision tree classification, Purity, Gini index, entropy Algorithms for constructing a decision tree Pruning methods to avoid over-fitting Regression trees
	Support vector machines Max margin motivation: low density, high stability Margin geometry to primal SVM formulation for separable training data (demo) Dual formulation and role of alpha in a form of sparse local regression Inseparable data, slack variables, hinge loss, upper bound on 0/1 training loss (demo) Handling non-linear regression by lifting data points to higher dimension (demo) Polynomial, Gaussian, RBF kernels Sequential minimal optimization (SMO) algorithm
	Feedforward Neural networks Feedforward networks Backpropagation Algorithm
	Neural Network Architectures: CNNs Motivation Basic convolution operation. Pooling. Architecture for basic image classification task.
	Neural Architectures for Sequences RNNs Transformers
	Combining models Bagging Random Forests by Breiman.
	Dimensionality Reduction Principal component analysis (PCA) Basic PCA, Eigenvalue and eigenvector recap, demo Probabilistic PCA EM algorithm for PCA Using kernels in PCA Eigen-SVD connection (Undirected) graph Laplacian Spectral graph partitioning, ratio cuts
	Unsupervised learning Clustering, k-means (Chapter 14.3 of HTF, Chapter in Bis07, Online copy of Anil Jain's book), online demo Unsupervised representation learning
	Reinforcement Learning Policy gradients
	LLMs: Foundation Models for Text Pre-training In-context learning RLHF
	Generative models for images Overview of text to image generators.