<p>Lecture, three hours. Requisites: courses 115A, 131A, or equivalent. Introduction to mathematical and theoretical aspects of machine learning and artificial intelligence. Topics include probability and high-dimensional statistics, neural networks, deep learning, artificial intelligence, modern architectures, approximation theory, optimization, and error estimates. S/U or letter grading.</p>

Mathematics

College of Letters and Science

Container 1

MATH 277 - Foundations of Machine Learning and Artificial Intelligence

Foundations of Machine Learning and Artificial Intelligence

Apply machine learning techniques to research in their chosen field.



Analyze the approximation properties of popular neural network architectures.

Provide theoretical justification for popular optimization routines in the nonconvex setting.

Present and analyze transformer-based approaches.

Explain the differences between commonly used neural network architectures.	

Discuss theoretical aspects of machine learning and artificial intelligence.

Discuss difficulties in high-dimensional learning problems using probability and geometry.

Discuss examples and limitations of the methods in the course

Explain the differences between shallow and deep architectures.

Explain and justify convolutional network architectures.

Apply optimization techniques for training models.

General Catalog

Foundations of Machine Learning and Artificial Intelligence

MATH 277

4 units