Course syllabus

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).


Yun Sing Koh’s part of the paper will cover a number of techniques and algorithms commonly used in data mining and machine learning, beginning with topics such as simple unsupervised learning and ending up with more recent topics such as data stream mining. The objective is not only to present the modern machine learning methods but also to analyse and evaluate the basic intuitions behind the methods as well as, a more formal understanding of how and why they work.

In Pat Riddle’s part of the paper, we will study several techniques for learning such as Ensemble Learning and Neural Networks. In addition, we will provide an overview of the experimental methods necessary for understanding machine learning research.

In Ian Waton’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).

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, Matrix Factorization, and Privacy.

Assessment

40% internal assessments, 60% exam

Teaching Staff

 

Pat Riddle (coordinator)
Room: 490, Computer Science Building (Building 303S)
Phone: 373-7599, Ext 87093 Email: pat@cs.auckland.ac.nz 

Joerg Wicker 
Room: 526, Computer Science Building (Building 303)
Phone: 373-7599, Ext 82184 Email: j.wicker@auckland.ac.nz 

Yun Sing Koh
Room: 485, Computer Science Building (Building 303S)
Phone: 373-7599, Ext 88299 Email: ykoh@cs.auckland.ac.nz 

Ian Watson
Room 493, Computer Science Building (Building 303S)
Phone 373-7599, Ext 88976 Email: ian@cs.auckland.ac.nz 

 

Suggested text


T. Mitchell, Machine Learning, McGraw Hill, 1997.

 

Documents

Course summary:

Date Details Due