Course Description
An in-depth introduction to machine learning methods and an exploration of research problems in machine learning and its applications which may lead to work on a project or a dissertation.
Athena Title
MACHINE LEARNING
Prerequisite
CSCI(PHIL) 4550/6550 or CSCI 4560/6560 or permission of department
Semester Course Offered
Not offered on a regular basis.
Grading System
A - F (Traditional)
Course Objectives
Machine learning is a subfield of artificial intelligence which is concerned with computer programs that can automatically improve their capabilities and/or performance by acquiring (learning) experience. The field has been growing steadily and producing many impressive applications in numerous fields of science and technology, including autonomous vehicles and robots, medical diagnosis, information retrieval and filtering tools and security risk detection. The main objectives of this course are: -An in-depth introduction to machine learning theory and methods -Exploration of research problems in machine learning and its applications which may lead to work on a project or a dissertation. The course is intended primarily for computer science and artificial intelligence graduate students. Graduate students from other departments who have a strong interest and sufficient experience in artificial intelligence may also find the course interesting.
Topical Outline
* Part I: Machine learning techniques Selected from inductive learning, decision trees, neural network approaches, evolutionary computation approaches and classifier systems, reinforcement learning, statistical and Bayesian learning, instance-based learning, explanation-based learning and computational learning theory. * Part II: Machine learning application Selected from data mining, medical diagnosis, fraud detection, pattern recognition and/or other contemporary applications.
Syllabus
Public CV