## 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.

## 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.

## 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).

## 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.

## 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.