CS480/680 - Introduction to Machine Learning

CS480/680 Lecture 1: Course Introduction

CS480/680 Lecture 2: K-nearest neighbours

CS480/680 Lecture 3: Linear Regression

CS480/680 Lecture 4: Statistical Learning

CS480/680 Lecture 5: Statistical Linear Regression

CS480/680 Lecture 6: Tools for surveys (Paulo Pacheco)

CS480/680 Lecture 6: Kaggle datasets and competitions

CS480/680 Lecture 6: Normalizing flows (Priyank Jaini)

CS480/680 Lecture 6: Unsupervised word translation (Kira Selby)

CS480/680 Lecture 6: Fact checking and reinforcement learning (Vik Goel)

CS480/680 Lecture 6: Sum-product networks (Pranav Subramani)

CS480/680 Lecture 6: EM and mixture models (Guojun Zhang)

CS480/680 Lecture 6: Model compression for NLP (Ashutosh Adhikari)

CS480/680 Lecture 7: Mixture of Gaussians

CS480/680 Lecture 8: Logistic regression and generalized linear models

CS480/680 Lecture 9: Perceptrons and single layer neural nets

CS480/680 Lecture 10: Multi-layer neural networks and backpropagation

CS480/680 Lecture 11: Kernel Methods

CS480/680 Lecture 12: Gaussian Processes

CS480/680 Lecture 13: Support vector machines

CS480/680 Lecture 14: Support vector machines (continued)

CS480/680 Lecture 15: Deep neural networks

CS480/680 Lecture 16: Convolutional neural networks

CS480/680 Lecture 17: Hidden Markov Models

CS480/680 Lecture 18: Recurrent and recursive neural networks

CS480/680 Lecture 19: Attention and Transformer Networks

CS480/680 Lecture 20: Autoencoders

CS480/680 Lecture 21: Generative networks (variational autoencoders and GANs)

CS480/680 Lecture 22: Ensemble learning (bagging and boosting)

CS480/680 Lecture 23: Normalizing flows (Priyank Jaini)

CS480/680 Lecture 24: Gradient boosting, bagging, decision forests