Instructor: Honglak Lee Coverage The goal of machine learning is to develop computer algorithms that can learn from data or past experience to predict well on the new unseen data. In the past few decades, machine learning has become a powerful tool in artificial intelligence and data mining, and it has made major impacts in many realworld applications.
This course will give a graduatelevel introduction of machine learning and provide foundations of machine learning, mathematical derivation and implementation of the algorithms, and their applications. Topics include supervised learning, unsupervised learning, learning theory, graphical models, and reinforcement learning. This course will also cover recent research topics such as sparsity and feature selection, Bayesian techniques, and deep learning. In addition to mathematical foundations, this course will also put an emphasis on practical applications of machine learning to artificial intelligence and data mining, such as computer vision, data mining, speech recognition, text processing, bioinformatics, and robot perception and control. The course will require an openended research project. Textbook(s) Bishop, Christopher M. Pattern Recognition and Machine Learning. New York, NY: Springer, 2006. Syllabus
Topics to be covered (tentative)
 Introduction (1 lecture)
 Overview
 Probability review
 Loss function
 Maximum likelihood
 MAP
 Regression (2 lectures)
 Linear regression
 Gradient descent and stochastic gradient
 Newton method
 Probabilistic interpretation of linear regression: Maximum likelihood
 Classification (2 lectures)
 knearst neighbors (kNN)
 Naive Bayes
 Linear discriminant analysis/ Gaussian discriminant analysis
 Logistic regression
 Generalized linear models, softmax regression
 Kernel methods (4 lectures)
 Kernel density estimation, kernel regression
 Support vector machines
 Convex optimization
 Gaussian processes
 Regularization (2 lectures)
 L2 regularization
 L1 regularization, sparsity and feature selection
 BiasVariance tradeoff
 Overfitting
 Cross validation, model selection
 Advice for developing machine learning algorithms
 Neural networks (1 lecture)
 Perceptron
 MLP and backpropagation
 Learning theory (2 lectures)
 Sample complexity
 VC dimension
 PAC learning
 Error bounds
 Graphical models (4 lectures)
 Bayesian networks
 Representation
 Exact inference
 Sampling based inference
 Learning in Bayesian networks
 Maximum likelihood estimation
 Expectation maximization
 Hidden Markov Models (HMM)
 Structure learning
 Bayesian inference and learning
 Markov networks
 Unsupervised learning (4 lectures)
 Clustering: Kmeans
 Gaussian mixtures
 Expectation Maximization (revisited)
 PCA
 Dimensionality reduction: ISOMAP, LLE
 ICA
 Sparse coding
 Boltzmann machines and autoencoders, Deep belief networks
 Reinforcement learning (3 lectures)
 MDP
 Value iteration and policy iteration
 Dynamic programming
 Value function approximation
 TD learning
