(Jan 8) The venue for tomorrow's class is changed to ERB 703.
(Jan 6) Welcome to this course.
Instructor: W. Y. Keung
Office: HCA320
Email: wykeung@cse.cuhk.edu.hk
Time & Venue
Tuesday 4:30-6:15pm, ERB407
Thursday 8:30-10:15am, SHB924
This course is designed for students to learn the principles of data mining and information processing via a hands-on approach. The first half of the course focuses on the fundamentals of data collection and processing, including the mathematical foundation and the representation of audio, visual and multimedia data. In the second half of the course, classical data mining methods will be introduced, along with their applications in datasets with meaningful physical interpretations. Students are advised to have a background in programming and a basic understanding of college-level mathematics.
At the end of the course of studies, students will be able to:
understand and explain the ideas of data mining and information processing;
organize raw data into suitable format for further analysis;
analyze data and extract information with traditional techniques and algorithms;
implement some algorithms learned from the course to solve real-world problems.
An overview on data mining and information processing; Audio and visual data collection and representation; Matrix algebra and distance metric; Elements in optimization; Blind source separation via convex optimization; Supervised learning methods: the perceptron, support vector machine, decision tree and probabilistic classifiers; Unsupervised methods: clustering, dimension reduction and pattern mining. Advanced topics with a theme on compression.
Assignment (15%)
Laboratory (25%)
Quizzes (20%)
Quiz 1: Feb 20
Quiz 2: Apr 3
Final Exam (40%)
Available on Blackboard