MATH4267: Deep Learning and Artificial Intelligence

• Machine Learning and Neural Networks (MATH3431)

• None

• None

• To provide advanced methodological and practical knowledge in the field of deep learning and artificial intelligence, covering a wide range of the modelling and computational techniques ubiquitous in recent scientific and technological applications.

• Multilayer perceptrons.
• Deep networks: CNNs, space, and computer vision; RNNs, time, and language processing.
• SGD and variants, dropout, etc.
• Network design.
• Programming deep networks.
• Extensions: autoencoders, GANs, or reinforcement learning.

Subject-specific Knowledge:

• a systematic and coherent understanding of the mathematical theory underlying deep neural networks and their training;
• an understanding of the relationship of this theory to other statistical techniques;
• the ability to make appropriate modelling and algorithmic choices for a given problem or application;
• the ability to implement those choices using currently available software packages, and test their validity and performance;
• sufficient understanding and expertise to be able to expand their knowledge of theory and practice to encompass newly developed techniques and software.

Subject-specific Skills:

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

Key Skills:

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

• Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
• Computer practicals consolidate the studied material and enhance practical understanding.
• 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.
• The written project report assesses the ability to implement the concepts introduced in the module using statistical software, to apply them in the analysis of a realistic problem, and to report scientific outputs in a clear and structured way.
• The end-of-year examination assesses the knowledge acquired and the ability to solve predictable and unpredictable problems.

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