Data-mining and Machine Learning: COMPSCI 760 Semester 1, City Campus

This course presents advanced materials on machine learning, search algorithms, heuristics, and planning

A brief outline of the course:
Machine learning techniques are widely used in many computing applications; for example, in web search engines, spam filtering, speech and image recognition, computer games, machine vision, credit card fraud detection, stock market analysis and product marketing applications. Machine learning implies that there is some improvement that results from the learning program having seen some data. The improvement can be in terms of some performance program (e.g., learning an expert system or improving the performance of a planning or scheduling program), in terms of finding an unknown relation in the data (e.g., data mining, pattern analysis), or in terms of customizing adaptive systems (e.g., adaptive user-interfaces or adaptive agents).

In Pat Riddle's part of the paper, we will provide an overview of the learning problem and the view of learning as search. We will study several techniques for learning such as Ensemble Learning, Bayesian Learning, Genetic Algorithms, Reinforcement Learning, and Neural Networks. In addition, we will provide an overview of the experimental methods necessary for understanding machine learning research.

In Joerg Wicker's part of the paper, we will cover further recent research topics in machine learning and data mining. Specifically, we will address Multi-Label and Multi-Target Learning, Recommender Systems and Matrix Factorization, Privacy, Online Learning, and Time Series.

In Ian Watson's part of the paper, we will cover case-based reasoning, recommender systems, explainable artificial intelligence (XAI), and recent case-studies of applied ML (time permitting).

Assessment: 40% internal assessments, 60% exam

Coordinator: Dr Patricia Riddle (supervisor)

Lecturers: Dr Joerg Wicker, Assoc Prof Ian Watson

For lecture slides / handouts - go to Lectures tab.

Suggested text: T. Mitchell, Machine Learning, McGraw Hill, 1997.

Course Information A copy of the course information sheet containing contact information for staff, staff office hours, assessment summary, how to catch up on a missed lecture or lab, how to seek assistance, and other course information is available here.