EE 364A: Convex Optimization

This is an archive of materials used for CS 228T, taught at Stanford in 2011 under Daphne Koller.

Course description

Machine learning is a field at the intersection of computer science and statistics that aims to develop computational systems that learn from data and improve with experience. Though its origins lie in the field of artificial intelligence, modern machine learning has transformed a huge variety of areas, such as biology, medicine, e-commerce, retail, marketing, operations, logistics, politics, journalism, and, of course, finance.

This course provides a general introduction to machine learning with a view towards applications in finance. The goal is to provide both a solid grounding in the foundations of machine learning as well as a conceptual map of the field and its relation to areas like statistics and optimization. The focus is on mathematical and conceptual understanding; the course will occasionally touch on implementation issues and financial examples, but will not emphasize either aspect in coursework.

Topics include linear regression, logistic regression, exponential families, generalized linear models, generative models, support vector machines, loss functions and regularization, sparsity, Bayesian methods, model selection, the EM algorithm, clustering, principal components analysis, and convex optimization and optimization algorithms.

Prerequisites

The course requires background in linear algebra, probability, and optimization at the level of MATH 2940, ORIE 5500, and ORIE 5300.

Syllabus

The syllabus may be adjusted through the course of the semester.

Homework

Quizzes

Readings

These readings will be posted intermittently through the semester and are entirely optional. Their goal is to give some exposure to the history, culture, and debates of machine learning, statistics, and data science, and to give additional perspective. Some are just included for historical interest and are not intended to be read cover to cover.