Advanced Machine Learning

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• State-of-the-art algorithms such as Support Vector Machines and Bayesian networks
• Key concepts, tools and approaches for pattern recognition on complex data sets
• Kernel methods for handling high dimensional and non-linear patterns
• Theoretical concepts and the motivations behind different learning frameworks

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Be able to solve real-world machine learning tasks: from data to inference

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Conceptually understand the role of pattern analysis and probabilistic modeling, together with the mathematical techniques this requires

Key concepts

• Supervised/Unsupervised Learning
• Loss functions and generalization
• Probability Theory
• Elements of Computational Learning Theory

Kernel Methods for non-linear data

• Support Vector Machines
• Kernels

Bayesian methods for using prior knowledge and data

• Bayesian inference
• Bayesian Belief Networks and Graphical models
• Gaussian Processes

Ensemble Learning

• Bagging
• Boosting
• Random Forest

Resources & Reading list

Internet Resources

Reasoning and Machine Learning.

Information Theory, Inference, and Learning Algorithms.

Textbooks

John Shawe-Taylor and Nello Cristianini (2004). Kernel Methods for Pattern Analysis. Cambridge University Press.

Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer.

Aurelien Geron. Hands-On Machine Learning with Scikit-Learn & Tensor Flow.

Summative

Summative assessment description

Referral

Referral assessment description

Repeat

Repeat assessment description

Repeat Information

Repeat type: Internal & External