Machine Learning (934G5)
Machine Learning
Module 934G5
Module details for 2024/25.
15 credits
FHEQ Level 7 (Masters)
Module Outline
This module exposes students to advanced techniques in machine learning. A systematic treatment will be used based on the following three key ingredients: tasks, models and features. Students will be introduced to both regression and classification and concepts such as model performance, learnability and computational complexity will be emphasized. Taught techniques will include: probabilistic and non-probabilistic classification and regression methods and reinforcement learning approaches including the non-linear variants using kernel methods. Techniques for pre-processing of the data (including PCA) will be introduced. Students will be expected to be able to implement, develop and deploy the techniques to real-world problems.
Prerequisite: Mathematics & Computational Methods for Complex Systems (817G5) or equivalent mathematical module / prior experience.
(MSc Computer Science (conversion) students can only taken this module if 817G5 Mathematics & Computational Methods for Complex Systems is taken in Semester 1).
Pre-Requisite
Mathematics & Computational Methods for Complex Systems (817G5) or equivalent mathematical module / prior experience.
[MComp Computer Science students are required to take this module if G6078 Game Design and Development was taken in year 2].
Module learning outcomes
Identify the strengths and weaknesses of state-of-the-art supervised, unsupervised, and reinforcement machine learning models including multi-layer perceptron, support vector machine, random forest, K-means, PCA, and Q-learning.
Critically analyse and implement several stochastic optimization methods ranging from stochastic gradient descent, stochastic variance reduction, to adaptive gradient methods for training machine learning models on big data.
Demonstrate knowledge of the fundamental principles of advanced machine learning models including probabilistic graphical models and statistical network models.
Apply developed classification/regression techniques with stochastic optimization to real-world problems, including extracting deep convolutional neural network features and incorporating prior knowledge.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | A2 Week 1 | 100.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Laboratory | 1 hour | 11111111111 |
| Spring Semester | Lecture | 2 hours | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr Temitayo Olugbade
Assess convenor
/profiles/272464
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