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BSc Computer Science and Mathematics / Course details
Year of entry: 2022
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Course unit details:
|Unit level||Level 2|
|Teaching period(s)||Semester 2|
|Offered by||Department of Computer Science|
|Available as a free choice unit?||No|
|Unit title||Unit code||Requirement type||Description|
|Foundations of Pure Mathematics B||MATH10111||Pre-Requisite||Compulsory|
|Mathematical Techniques for Computer Science||COMP11120||Pre-Requisite||Compulsory|
- Describe fundamental concepts in machine learning (including supervised and unsupervised learning, classification, regression and clustering), and essential elements for building a machine learning system.
- Explain key supervised learning techniques, compare their differences (including limitations and advantages, quality and trade-offs, commercial/industrial concerns). Apply the knowledge to select suitable techniques for a particular application.
- Explain key clustering algorithms and their applications.
- Describe fundamental concepts in model evaluation and selection, explain the training, validation and testing processes, hyperparameter selection approaches. Apply the knowledge to use data, design machine learning experiments, and make observations from results.
- Design and implement machine learning solutions to real-world problems, evaluate the solution, analyse results and implication.
- Recognise and describe issues in machine learning.
- Machine Learning Basics
- k-Nearest Neighbours
- Machine Learning Experiments
- Machine Learning Models
- Loss Functions
- Training and Optimisation
- Artificial Neural Networks
- Support Vector Machines
- Clustering Analysis
- Deep Learning Models
Teaching and learning methods
Weekly lectures with structured input and exploratory activities. These will be organised as a blend of brief presentations, tutorial question and practice activities, discussions of materials and tasks that are available online, and question-answer sessions.
Bi-weekly laboratories will be drop-in help desks where GTAs provide support for problems provided in lab scripts. These will also be used as surgeries to provide feedback on assessments and as an opportunity to ask questions about the set tasks, and learning materials. Lab scripts contain assessments on mathematical programming for supporting basic machine learning model implementation, also design, implementation and analysis of machine learning techniques for real-world applications.
- Analytical skills
- Project management
- Problem solving
- Written communication
|Practical skills assessment||50%|
Cohort-level feedback after marking and individual feedback provided by GTA upon request.
|Scheduled activity hours|
|Assessment written exam||2|
|Practical classes & workshops||12|
|Independent study hours|
|Tingting Mu||Unit coordinator|