DS 300 Data Mining
This project-based course aims to cover the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This course consists of three main modules: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehousing, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for regression, classification, clustering, dimensionality reduction and association; and (3) Deep Learning, which discusses the state-of-art deep learning techniques such as CNN and RNN with the implementation in Tensorflow.
Instructor:
Dr. Peilong Li
Office:
Esbenshade 284B
Appointments:
By email
Number of Credits
4
Pre-requisites
- DS 200 Introduction to Data science
- CS 309 Database Systems
Textbooks
- (Required) Aurelien Geron.
Hands-on Machine Learning with Scikit-Learn, Keras & Tensorflow.
2nd Edition. 2019, O’Reilly.
ISBN-13: 978-1-492-0326-9
