CS 189/289A: Introduction to Machine Learning

Fall 2025 Frequently Asked Questions

Course Description

This class covers theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; deep learning models including CNNs, Transformers, graph neural networks for vision and language tasks; and Markovian models for reinforcement learning and robotics.

Offerings

Goals

Provide a rigorous foundation in the mathematics, algorithms, and concepts of machine learning.
Prepare students for advanced coursework and research in artificial intelligence, deep learning, computer vision, and natural language processing.
Enable students to implement machine learning algorithms and apply them to real-world problems.

Prerequisites

This course assumes strong preparation in mathematics and programming. The required prerequisites are:

Multivariable Calculus: MATH 53
Linear Algebra: MATH 54 or equivalent
Probability and Discrete Mathematics: COMPSCI 70 or equivalent

You should be comfortable with vector calculus (including gradients and the multivariate chain rule), matrix operations, probability theory (including conditional probability and Bayes’ rule), and writing/debugging complex programs in Python. If you lack preparation in these areas, you are likely to struggle.