MATH31520: Machine Learning and Neural Networks

• Data Science and Statistical Computation and Statistical Inference

• None

• None

• To provide advanced methodological and practical knowledge in the field of machine learning, covering a wide range of the modelling and computational techniques ubiquitous in recent scientific and technological applications, and to provide an introduction to neural networks.

• Function learning, loss functions, training.
• Bias-variance decomposition, overfitting.
• Regression and classification problems.
• Linear and non-linear learning.
• Model selection and cross validation.
• Shrinkage methods.
• Kernels and SVMs.
• Ensemble learning (boosting, bagging, random forests).
• Feature engineering.
• Intro to neural networks.
• Extensions: super learners.

Subject-specific Knowledge:

• By the end of the module students will:
• have a systematic and coherent understanding of the mathematical theory underlying a variety of machine learning techniques;
• have an understanding of the relationship of this theory to other statistical techniques;
• be able to make appropriate modelling and algorithmic choices for a given problem or application;
• be able to implement those choices in software, and test their validity and performance;
• have an elementary understanding of the functioning and uses of neural networks.

Subject-specific Skills:

• Students will have mathematical skills in the following areas: modelling, optimization, computation.

Key Skills:

• Students will have skills in the following areas: problem formulation and solution, critical and analytical thinking, computer programming.

• Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
• Problem classes show how to solve example problems in an ideal way, revealing also the thought processes behind such solutions.
• Computer practicals consolidate the studied material, explore theoretical ideas in practice, enhance practical understanding, and develop practical data analysis skills.
• Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
• Formative assessments provide feedback to guide students in the correct development of their knowledge and skills in preparation for the summative assessment.
• Computer-based examinations assess the ability to use statistical software and basic programming to solve predictable and unpredictable problems.
• The end-of-year examination assesses the knowledge acquired and the ability to solve predictable and unpredictable problems.

Eight written or electronic assignments to be assessed and returned.Other assignments are set for self-study and complete solutions are made available to students.

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