Module 06-02640 (2010)
|Ata Kaban||Semester 1||10 credits|
Co-ordinator: Ata Kaban
Reviewer: Xin Yao
The Module Description is a strict subset of this Syllabus Page.
The aims of this module are to:
- introduce the basic concepts and terminology of machine learning
- give an overview of the main approaches to machine learning (with the exception of neural nets, which are dealt with in a separate module)
- show similarities and differences between different approaches
- present basic principles for the classification of approaches to machine learning
- give practical experience of applying machine learning algorithms to classification and data analysis problems
On successful completion of this module, the student should be able to:
- demonstrate a knowledge and understanding of the main approaches to machine learning
- demonstrate the ability to apply the main approaches to unseen examples
- demonstrate an understanding of the differences, advantages and problems of the main approaches in machine learning
- demonstrate an understanding of the relationship between machine learning and human learning
- demonstrate an understanding of the main limitations of current approaches to machine learning, and be able to discuss possible extensions to overcome these limitations
- demonstrate a practical understanding of the use of machine learning algorithms
- 06-20236 - Machine Learning (Extended)
2 hrs lectures, exercise and laboratory classes per week.
- Sessional: 1.5 hr examination (80%), continuous assessment (20%).
- Supplementary: By examination only.
- Basic notions of learning, introduction to learning algorithms, literature
- Probabilistic Sequence models (Random sequence model, 1st order Markov model)
- Reinforcement Learning in deterministic environments
- Reinforcement Learning in non-deterministic environments
- Unsupervised Learning; Dimensionality reduction; Latent Semantic Analysis
- Independent Component Analysis. The cocktail party problem.
- Introduction to Bayesian learning; Naive-Bayes classification
- Decision Tree Learning
- Instance-Based Learning (k-nearest neighbour, Case-based Reasoning)
- Support Vector and Kernel Machines
- Clustering and visual data analysis