CSCI4230 Computational Learning Theory


Course code CSCI4230
Course title Computational Learning Theory
Course description This course introduces theoretical foundations of efficient learning algorithms and their limitations. Topics include Probably Approximately Correct learning, Occam learning, Vapnik–Chervonenkis dimension, boosting, Statistical Query learning, active learning, and crypotgraphic hardness of learning.
本科介紹各款學習算法之理論根基與局限。內容涉及「多數大致正確」和「奧坎」學習模型、Vapnik–Chervonenkis 維數、提升法、統計提問學習、主動學習、密碼學引致的學習困難。
Unit(s) 3
Course level Undergraduate
Pre-requisite  ENGG2430 or 2450 or 2760 or 2780 or ESTR2002 or 2005 or 2018 or 2020 or 2308 or 2362 or IERG2470 or MIEG2440
Semester 1 or 2
Grading basis Graded
Grade Descriptors A/A-:  EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions.
B+/B/B-:  GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered.
C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems.
D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems.
F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course.
Learning outcomes At the end of the course of studies, students will have acquired the ability to
1. identify the mathematical models in various learning applications
2. analyze the performance of different learning algorithms
3. understand the relative computational hardness of various learning problems
(for reference only)
Essay test or exam: 50%
Assignments: 50%
Recommended Reading List 1. Michael J. Kearns and Umesh Vazirani, An Introduction to Computational Learning Theory


CSCIN programme learning outcomes Course mapping
Upon completion of their studies, students will be able to:  
1. identify, formulate, and solve computer science problems (K/S); T
2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S);
3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V);
4. communicate effectively (S/V);
5. succeed in research or industry related to computer science (K/S/V);
6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S); T
7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V);
8. practise high standard of professional ethics (V);
9. draw on and integrate knowledge from many related areas (K/S/V);
Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured