Machine Learning, the field of study that gives computers the ability to learn from data, is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning. This course gives a graduate-level introduction to machine learning and in-depth coverage of new and advanced methods in machine learning, as well as their underlying theory. It emphasizes approaches with practical relevance and discusses a number of recent applications of machine learning, such as Data Mining (in Big Data / Data Science, Data Analytics), Natural Language Processing, Computer Vision, Robotics, Bioinformatics and Text and Web data processing. Machine Learning is used in various industries including Financial Services, Oil & Gas, Health Care, Marketing & Advertising, Government, Internet and Internet of Things.

This course covers a variety of learning paradigms, algorithms, theoretical results and applications. It uses basic concepts from artificial intelligence, information theory, statistics, and control theory insofar as they are relevant to machine learning. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines, decision tree, Bayesian learning & optimization); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. Other topics include HMM (Hidden Markov Model), Evolutionary Computing, Deep Learning (With Neural Nets) and designing algorithms whose performance can be rigorously analyzed for fundamental machine learning problems.

An important part of the course is a group project. Major open source tools used for parallel, distributed and scalable machine learning will be briefly covered to help students doing the projects. (4 units) Prerequisite: None.