Pattern Recognition and Machine Learning

This page provides the course overview and lecture materials for the fall semester of the 2026–2027 academic year. Follow the lecture sequence below for preparation, practice, and review.

Instructor

Chizhi Chris ZHANG

Affiliation

Advanced Computing and Digital Technology Research Center,
Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP), Chinese Academy of Sciences
University of Chinese Academy of Sciences

Course Structure

18 teaching meetings, followed by review and an open-book examination.

Course Overview

Learning from examples.

The course covers data analysis, generative and discriminative classifiers, feature engineering, statistical learning theory, linear models, and support vector machines. It then introduces clustering, dimensionality reduction, semi-supervised learning, ensemble methods, and deep learning, including neural networks, CNNs, sequence models, and Transformers.

Course Schedule

20 meetings across the semester.

Meetings 1–18: teaching.
Meeting 19: review.
Meeting 20: open-book examination.

Lecture Materials

Lecture Slides and Printable Scripts

Lectures 01–18 are available as PDF slides and Chinese printable scripts, with three 45-minute class periods per lecture. Updated 27 October 2026.

Download all 18 lectures and scripts (ZIP · 15.7 MB)

Lecture 01 — Overview: Pattern Recognition and Machine Learning · 37 slides