Description

This course begins with a brief review of linear algebra, probability, and statistics, and then introduces both algorithms and applications of machine learning. The course covers the basic algorithms of supervised maching learning and unsupervised learning, including linear regression, neural networks, decision trees, and support vector machines. Through the course, we will get familiar with applications in computer vision and natural language processing, such as image classificaton and large language models.

Lectures

Office hours and contact information

Topics

1. Brief Review of Linear Algebra, Probablity and Statistics
2. Machine Learning Theory and Algorithnms
3. Machine Learning Applications in Computer Vision and Natural Language Processing
4. Guest Lectures [TBD]

Text books

Prerequisites

Grading