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Course Module Information
Course Modules
CT5165: Principles of Machine Learning
Semester 1 | Credits: 5
Machine Learning is concerned with algorithms that improve their performance over time, as they are exposed to new data. This module introduces learners to the different categories of machine learning task and provides in-depth coverage of important algorithms for tackling them. Its focus is on the theory underlying ML algorithms. In addition, the learners gain experience of implementing algorithms from scratch, as well as using ML software tools to select and applying these algorithms in applications, and they evaluate and interpret the results.
Topics include:
1. Overview of Machine Learning & Major Categories of Task
2. Supervised Learning Principles and Information-Based Learning
3. Similarity-Based Learning
4. Evaluating Classifier Performance, Practical Advice, and Some Machine Learning Tools
5. Linear Regression in One and Multiple Variables
6. Linear Classifiers with Hard and Soft Thresholds
7. Probabilistic Machine Learning
8. Reinforcement Learning.
(Language of instruction: English)
Learning Outcomes
- Define Machine Learning and explain what major categories of learning task entail
- Demonstrate how to apply the machine learning and data mining process to practical problems
- Explain and apply algorithms including decision tree learning, instance-based learning, probabilistic learning, linear regression, logistic regression, Q-learning, and others
- Given a dataset and task to be addressed, select, apply and evaluate appropriate algorithms, and interpret the results
- Discuss ethical issues and emerging trends in machine learning.
Assessments
- Written Assessment (60%)
- Continuous Assessment (40%)
Teachers
- Ihsan Ullah:
Research Profile |
Email
Note: Module offerings and details may be subject to change.