Spring 2019

UCLA STAT161/261: Introduction to Machine Learning

This course provides an accessible introduction to machine learning aimed at advanced undergraduate and graduate students in statistics, computer science, electrical engineering or related disciplines. Topics covered include Bayes decision theory, parameter estimation, regression, PCA, K-means, SVMs and hidden Markov models. Emphasis is on learning high-level concepts behind machine learning algorithms and gaining practical experience applying them to real data and problems.

• Lecture notes

Spring 2018

UCLA STAT161/261: Introduction to Machine Learning

This course provides an accessible introduction to machine learning aimed at advanced undergraduate and graduate students in statistics, computer science, electrical engineering or related disciplines. Topics covered include Bayes decision theory, parameter estimation, regression, PCA, K-means, SVMs and hidden Markov models. Emphasis is on learning high-level concepts behind machine learning algorithms and gaining practical experience applying them to real data and problems.

• Lecture notes and homework assignments

Fall 2017

UCLA STATS 200a: Applied Probability

This course provides an introduction to probability theory and probability models, which are critical to understanding the tools of statistics. The assumed mathematical background of the course is a working knowledge of multivariable calculus and linear algebra. Topics include probability spaces, basic combinatorics, discrete and continuous random variables, expectation, classical distributions in probability and statistics, multivariable densities, central limit theorem, selected topics in mathematical statistics (time permitting).

• Syllabus

Spring 2016

UCLA STAT161/261: Introduction to Machine Learning

This course provides an accessible introduction to machine learning aimed at advanced undergraduate and graduate students in statistics, computer science, electrical engineering or related disciplines. Topics covered include Bayes decision theory, parameter estimation, regression, PCA, K-means, SVMs and hidden Markov models. Emphasis is on learning high-level concepts behind machine learning algorithms and gaining practical experience applying them to real data and problems.

• Class web site

Winter 2013, 2014 and 2015 (UCSC)

UCSC EE262: Detection and Estimation

Covers fundamental approaches to designing optimal estimators and detectors of deterministic and random parameters and processes in noise, and includes analysis of their performance. Binary hypothesis testing: the Neyman-Pearson Theorem. Receiver operating characteristics. Deterministic versus random signals. Detection with unknown parameters. Optimal estimation of the unknown parameters: least square, maximum likelihood, Bayesian estimation. The course includes review of the fundamental mathematical and statistical techniques employed. Many applications of the techniques are presented throughout the course.

• Course Description

• Course catalog listing

Fall 2013

UCSC CE/EE293: Sparsity, Dimensionality Reduction, and Machine Learning

Special topics class in sparsity and low-rank methods in approximation, inverse problems and machine learning. Topics included sparse inverse problems, convex relaxations, random matrix theory, optimization methods, dictionary learning and applications in image processing and learning.

Spring 2013,

UCSC CE/EE153: Digital Signal Processing

UCSC CE/EE103: Signals and Systems Introduction to signals and analog and digital signal processing, a topic that forms an integral part of systems in many diverse areas, including seismic data processing, communications, speech processing, image processing, neuroscience, and electronics. Signal and system representations, the Fourier transform, filter and the Laplace transform are all covered.