|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||Written analysis of scientific paper(s) (3000 words limit), followed by and oral presentation and discussion on the same.||20%|
|Semester Assessment||Written assignment contrasting the use of two methods discussed in the course, applied to data provided by the student. (3000 words)||80%|
|Supplementary Assessment||Will take a form as agreed by Departmental exam board||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)
This module is at CQFW Level 7