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

Download Lecture Notes

Lectures 01 and 02 are available as PDFs. Materials for Lectures 03–18 will be added as the course progresses.

Lecture 01 — Overview: Learning from Examples

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Lecture 02 — From Lunch Orders to Data Analysis

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Lecture 03 — Generative Classifiers

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Lecture 04 — Discriminative Classifiers I

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Lecture 05 — Discriminative Classifiers II

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Lecture 06 — Feature Engineering

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Lecture 07 — Statistical Learning Theory

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Lecture 08 — Linear Models

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Lecture 09 — Support Vector Machines

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Lecture 10 — Clustering

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Lecture 11 — Dimensionality Reduction

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Lecture 12 — Semi-Supervised Learning

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Lecture 13 — Bagging and Random Forests

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Lecture 14 — Boosting, GBDT, and Stacking

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Lecture 15 — Neural Networks and Backpropagation

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Lecture 16 — Vision and CNNs

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Lecture 17 — Deep Training and Applications

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Lecture 18 — Sequences, Transformers, and New Advances

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