CS182: Introduction to Machine Learning

Course Descriptions

Machine learning (ML) is the science of making computer artifacts improve their performance without requiring humans to program their behavior explicitly. Machine learning has accomplished successes in a wide variety of challenging applications, ranging from computational molecular biology to computer vision to social web analysis.

CS182 Introduction to Machine Learning is an undergraduate-level introductory course in machine learning. It is not only suitable for students who want to apply principled machine learning techniques competently to their application-oriented research areas, but is also suitable for students pursuing or planning to pursue research in machine learning or other related areas that focus on model and algorithm development.

(Developed based on the course materials by Ethem Alpaydin.)

Announcements

1. Piazza: https://piazza.com/shanghaitech.edu.cn/fall2024/cs182

2. Gradescope: See the HW's.

Prerequisites

1. Compulsory: Linear Algebra, Calculus, Probability and Statistics, Programming.

2. Recommended Postrequisites: Matrix Analysis and Computations, Convex Optimization, Machine Learning.

Textbooks and Optional References

Textbooks

1. Ethem Alpaydin, Introduction to Machine Learning (4th Edition), The MIT Press, 2020.

References

1. Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification (2nd Edition), Wiley, 2000.

2. Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006.

3. Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.

4. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, Foundations of Machine Learning (2nd Edition), The MIT Press, 2018.

Schedule (Subject to Change)

Topics

1. Topic 0: Overview

2. Topic T1: ML Introduction

3. Topic T2: Mathematical Foundations of ML (Linear Algebra, Probability and Statistics, Optimization Theory, etc.)

4. Topic P1: “scikit-learn” Introduction and Data Preprocessing

5. Topic T3: Bayesian Decision Theory

6. Topic T4: Parameter Estimation for Generative Models

7. Topic T5: Linear Discrimination Models

8. Topic P2: Practice Course

9. Topic T6: Multilayer Perceptrons

10. Topic T7: Support Vector Machines

11. Topic T8: Dimensionality Reduction

12. Topic P3: Practice Course

13. Topic T9: Clustering and Mixture Models

14. Topic T10: Nonparametric Methods

15. Topic T11: Ensemble Learning

16. Topic P4: Practice Course

17. Topic T12: Model Assessment and Selection

18. Topic T13: Review

Note: All course materials are available on Piazza.com.

Assessment

30% assignments, 40% final exam, 30% final project.

Academic Integrity Policy

Group study and collaboration on problem sets are encouraged, as working together is a great way to understand new materials. Students are free to discuss the homework problems with anyone under the following conditions:

• Students must write down their own solutions. Plagiarism is never allowed. Similar answers, MATLAB codes, etc., found in HWs will invite you into suspected plagiarism investigation.

• Students must list the names of their collaborators (i.e., anyone with whom the assignment was discussed).

• Students can not use old solution sets from other classes under any circumstances, unless the instructor grants special permission.

Students are encouraged to read the ShanghaiTech Policy on Academic Integrity.