CS M146

Introduction to Machine Learning CS M146 (Spring 2027 @ UCLA)

An introduction to machine learning, covering the core algorithms and theory behind supervised, unsupervised, and generative learning. Topics span nearest-neighbor and tree-based methods, linear and logistic regression, perceptrons and neural networks, support vector machines and kernel methods, learning theory, dimensionality reduction, probabilistic and Bayesian models, clustering, and ensemble methods. Students gain both the mathematical foundations and hands-on experience implementing learning algorithms.

Instructor's bio: Shahriar Talebi is an Assistant Professor in the Department of Mechanical & Aerospace Engineering at the UCLA Samueli School of Engineering (since July 2025). Before UCLA, he was a Postdoctoral Research Fellow at Harvard University and a contributor to the NSF AI Institute in Dynamic Systems (Dynamics AI). He received a Ph.D. in control theory from the University of Washington in 2023—focusing on constrained decision-making and control in complex systems—and an M.Sc. in Mathematics (differential geometry) from UW.

Course Logistics

  • Cross-listed as: COM SCI M146 / STATS M146
  • Instructor: Dr. Shahriar Talebi, s.talebi@ucla.edu, office: Eng IV, 38-137F
  • Term: Spring 2027 @ UCLA
  • Lecture times, location, and office hours to be announced.

Reference Texts

  1. C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
  2. K. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.

Prerequisites

  • Probability and statistics
  • Linear algebra
  • Calculus and optimization
  • Programming experience (Python)

Planned Topics

The following outline reflects the general structure of prior offerings of this course and will be adapted for Spring 2027; exact lecture-by-lecture pacing is subject to change.

Table of Contents
Lecture Topic
1 Introduction to machine learning
2 Overview: the machine learning pipeline and foundational concepts
3 K-nearest neighbor
4 Decision trees
5 The perceptron algorithm
6 Logistic regression
7 Multi-class classification and linear regression
8 Multi-class logistic regression and neural networks
9 Neural networks
10 Neural networks and deep learning
11 Support vector machines
12 Kernel methods
13 Kernel methods and learning theory
14 Learning theory and clustering
15 Principal component analysis and embeddings
16 Bayesian learning
17 Naïve Bayes and Gaussian mixture models
18 The Expectation-Maximization (EM) algorithm
19 Ensemble methods; course wrap-up