Instructor: W. Y. Keung
Office: 12/F, Kowloon East Campus (KEC)
Email: wyokeung@hkuspace.hku.hk
Lecture Time & Venue:
Consultation Hours:
This is an introductory course in discovering useful patterns using data mining algorithms. Students will study the fundamental concepts of data collection, knowledge discovery and decision making to create business strategies, and learn the practical skills to employ data mining software to tackle cases in real world.
There is no hard prerequisite for CCIT4075. However, knowledge of/ experience in the following are preferable:
basic computer programming, say in python or C, which allows you to get into Octave scripting fast;
linear algebra: vector and matrix operations;
proofing techniques, e.g. from discrete mathematics/ maths for computing;
probability and statistics: basics, random variables, probability distribution etc.
Assignments (30%)
may contain Octave/Matlab programming problems
no late submission will be accepted unless prior consent with the instructor is sought
In-Class Quizzes (30%): In-class quizzes are to be held. You are allowed to consult i) a double-sided A4-sized cheat sheet and ii) a non-programmable calculator in quizzes. Scopes:
Quiz 1: Lecture 1-3
Quiz 2: Lecture 4-6
Quiz 3: Lecture 7-9
Final Exam (40%): to be centrally scheduled/announced
Lecture notes, as well as supplementary materials, will be provided; Please check this course website and SOUL regularly.
Main texts:
M. J. Zaki and W. Meirna Jr., Mining and Machine Learning: Fundamental Concepts and Algorithm, Cambridge, 2020.
Basics and Foundation:
Supervised methods:
Unsupervised methods:
Advanced Topics: