Module code: 934G5
Level 7 (Masters)
15 credits in spring semester
Teaching method: Laboratory, Lecture
Assessment modes: Coursework
In this module, you explore advanced techniques in machine learning.
You use a systematic treatment, based on the following three key ingredients:
As part of the module, you are introduced to both regression and classification, and your studies emphasise concepts such as model performance, learnability and computational complexity.
You learn techniques including:
- probabilistic and non-probabilistic classification and regression methods
- reinforcement learning approaches including the non-linear variants using kernel methods.
You are also introduced to techniques for pre-processing the data (including PCA).
You will then need to be able to implement, develop and deploy these techniques to real-world problems.
In order to take this module, you need to have already taken the 'Mathematics & Computational Methods for Complex Systems' module (817G5), or have taken an equivalent mathematical module or have equivalent prior experience.
Mathematics & Computational Methods for Complex Systems (817G5) or equivalent mathematical module / prior experience
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.