|Delivery Type||Delivery length / details|
|Seminars / Tutorials||30 hours|
|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||Written assessment of scientific papers (up to 3000 words), followed by an oral presentation and discussion.||40%|
|Semester Assessment||Report (up to 3000 words) on contrasting the application of (at least) two ML methods to an identified dataset.||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 implementation methods and tools used in the development of the types of autonomous system considered in this scheme.
Show proficiency in building autonomous systems using the appropriate tools.
Demonstrate skills in designing, running and documenting experiments using autonomous systems.
Demonstrate capability to write a detailed project proposal.
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|
This module is at CQFW Level 7