|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||Written analysis of scientific paper(s) (3000 words limit), followed by an oral presentation and discussion on the same.||40%|
|Semester Assessment||Written assignment contrasting the use of several methods discussed in the course, applied to data provided by the lecturers (4000 words).||60%|
|Supplementary Assessment||Resubmission of failed/nonsubmitted components or others of equivalent value.||100%|
On successful completion of this module students should be able to:
Demonstrate competence with the machine learning methods and tools considered in this scheme.
Show proficiency in analysing data sets using the appropriate tools.
Demonstrate skills in designing, running and documenting experiments using machine learning.
Demonstrate capability to write and present a detailed analysis of an application of machine learning.
1. What is Machine Learning? (2 hrs)
Foundations and assumptions of ML.
2. Supervised Learning. (2 hrs)
Learning from labelled examples.
3. Bayesian Decision Theory. (3 hrs)
Probability and optimality in learning.
4. Parametric Methods. (2 hrs)
5. Dimensionality Reduction. (2 hrs)
Detecting unnecessary attributes and removing them to improve accuracy.
6. Clustering. (3 hrs)
K-means, hierarchical, consensus clustering techniques.
7. Nonparametric Methods. (3 hrs)
Learning without constructing a model (esp. kNN); transductive learning.
8. Hidden Markov models. (3 hrs)
Probabilistic, structural models from data.
9. Assessing and Comparing Classification Algorithms. (3 hrs)
10. Combining Multiple Learners. (3 hrs)
Obtained improved results by combining the predictions of multiple classifiers.
11. Reinforcement Learning. (2 hrs)
Learning sequences of actions with reward.
Additional material requested by students via questionnaire. (2 hrs)
|Skills Type||Skills details|
|Application of Number||Inherent to subject|
|Improving own Learning and Performance||Inherent to subject|
|Information Technology||Inherent to subject|
|Personal Development and Career planning||Encourages students to see roles in subject for career and personal development|
|Problem solving||Inherent to subject|
|Subject Specific Skills||Representation and Reasoning for Intelligent Systems|
Reading ListRecommended Text
Alpaydin, Ethem. (2010.) Introduction to machine learning /Ethem Alpaydin. 2nd ed. MIT Press Primo search Mitchell, Tom M. (1997.) Machine learning /Tom M. Mitchell. McGraw-Hill Primo search Witten, I. H. (2005.) Data mining :practical machine learning tools and techniques /Ian H. Witten, Eibe Frank. 2nd ed. Morgan Kaufman Primo search
This module is at CQFW Level 7