DSAA5002

Data Mining and Knowledge Discovery in Data Science (Fall 2026)

Instructor: LI,Jia (office hour: We, 3:30PM -4:30pm)

TA: Chen, Aochuan & Wu, Xiaojun & Zi, Chenyi & Wang, Peisong & Zheng, Zinan & Liu, Jiaqi & Sun, Henan

Lecture Time: Mon 3:00 PM - 5:50PM

Venue: Lecture Hall C

Introduction

This course, will cover some basic algorithms in data mining and data science, including data preprocessing, classification (decision tree classifier, support vector machine, ensemble), clustering (k-means, hierarchical clustering, spectral clustering), anomaly detection, graph analytics, Association Analysis, PageRank, dimensionality reduction, EM algorithm, etc.

Announcements

Sep. 1 hi all.

Grading

Mid exam: 50%

Project: 50%

Reference and Handouts

Reference books and courses for extra reading:

[1] Introduction to Data Mining. Pang-Ning Tan, Michael Steinbach, and Vipin Kumar.

[2] Data Mining: Concepts and Techniques. Jiawei Han, Micheline Kamber, and Jian Pei.

[3] Foundations of Data Science. Avrim Blum, John Hopcroft, and Ravindran Kannan.

[4] Mining of Massive Datasets. Jure Leskovec, Anand Rajaraman, Jeff Ullman.

[5] CMPSC1 689: Machine Learning. Subhransu Maji.