CSCI 436/536: Data Mining

Instructor: Professor Huajie (Jay) Shao

Office: McGlothlin-Street Hall, office 140

Instructor Office hours: Mon/Wed 4:00-5:30 PM

[Zoom link]

Email: [email protected]

Lecture: 2:00-3:20 pm, M/W

Lecture Classroom: Blow Memorial Hall--Room:333

Discussion at Piazza:

https://piazza.com/wm/spring2024/csci436536

Teaching Assistant: Xiaochang Li ([email protected])

TA office hours: Tuesday 4:00-5:00 pm

TA Office Hour Zoom link

📜 Course Description

The past few years have witnessed big data boom in different areas, including commercial platforms, healthcare, social networks, business, finance, and more. Extracting useful and valuable information from big data can help improve quality of life and make our world a better place. The goal of this course is to introduce the fundamental concepts and techniques in data mining. Specifically, this course will cover the basic data mining concepts, graph mining, traditional clustering and classification models as well as the latest deep learning techniques. This course can help undergraduate students find a position as a data scientist after graduation and do some data mining-related projects for post-graduate study. In this course, students are required to do machine programming assignments and take midterm and final exams.

Course Outcomes for Undergraduates

For undergraduate students, they need to master the basic concepts and knowledge of machine learning and deep learning. After taking this class, they are required to apply the learned models in real-world applications. They do NOT need to learn some advanced AI models, like Transformer and BERT. In short, undergraduate students only need to learn applied machine learning and deep learning.

Course Outcomes for Graduate Students

For graduate students, they need to fundamentally understand the theory and concepts of state-of-the-art deep learning models, especially Transformer based on language models. In addition, they need to learn some optimization algorithms to train the deep learning models. Finally, they are asked to read some research papers and do a research project with a group of students. In sum, they need to delve into advanced machine-learning models and come up with some new ideas for the research project.

🎯Course Objectives

By the end of this course, you will be able to develop and apply data mining and deep learning techniques to do research projects or solve real-world industry problems.

🗝 Enrollment

Prerequisite(s): familiar with Python, Data Structure, and Algorithms. Have some background in Statistics, Math, or Machine Learning Co-Requisite(s): math 211 and 212 Recommended Books:

CSCI-536 Graduate Students ( Additional Project)

Final Project for Graduate Students (Group)