MATH42815: Machine Learning

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• To introduce the essential knowledge and skills required in machine learning for data science.

• Feature selection and regularization for big data (e.g. ridge and lasso regression).
• Flexible methods for fitting curves to data (e.g. polynomial regression and splines).
• Supervised machine learning (e.g. decision/classification trees, support vector machines, neural networks, deep learning).

Subject-specific Knowledge:

• By the end of the module students will:
• Be aware of a wide range of supervised learning methods.
• Have a systematic and coherent understanding of the theory, computation and application of the topics studied.
• Have acquired a coherent body of applicable knowledge on modern regression methods, decision-based machine-learning techniques, support vector machines, neural networks, and deep learning.

Subject-specific Skills:

• In addition, students will have acquired:
• Ability to use statistical software R to conduct synthesis of data and data analysis.
• Programming skills generally used in machine learning.
• Ability to identify and apply appropriate supervised learning methods to modern real-world problems.

Key Skills:

• Sufficient mastery of machine learning concepts and ability to apply them appropriately to real-world applications.
• Ability to clearly communicate statistical models and relevant conclusions through writing.
• Ability to organise, prioritise, and manage time effectively.
• Ability to advance and extend their knowledge through significant independent learning and research.

• This module will be delivered by the Department of Mathematical Sciences.
• Teaching will be delivered primarily by workshops and lectures.
• Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
• Workshops describe theory and its application to concrete examples, enable students to test and develop their understanding of the material by applying it to practical problems, and provide feedback and encourage active engagement.
• Workshops are a combination of live lectures, computer practicals, problem classes, tutorials and guided group work.
• Lectures and workshops will be supported by the distribution of materials such as video content, directed reading, e-assessments, reflective activities, opportunities for self-assessment, and peer-to-peer learning within a tutor-facilitated discussion board.
• Students will be able to obtain further help in their studies via scheduled office hours or surgeries as well as by approaching their lecturers by email.
• Students will be expected to work in between workshops and lectures, and to discuss their own work during the workshops. This work will be guided by the module leader, but will be organised by the students themselves, thereby enabling them to demonstrate their time management skills.
• Students will undertake independent research to further their knowledge of the topic and self-directed learning to further their technical and transferable skills.
• The workshops also provide opportunities for module leaders to monitor progress and to provide feedback and guidance on the development of ideas for the project, and for students to gauge their progress throughout the duration of the module.
• Student performance will be assessed through two individual assignments and four quizzes (e-assessments).
• The quizzes (e-assessments) enable the students to put into practice learning from lectures and strengthen their understanding.
• The assignments will provide the means for students to demonstrate their acquisition of subject knowledge and the development of their problem-solving skills.

Workshop discussion of students' ideas and experiences; informal discussions of student progress with module leader when necessary; interim feedback via continuous assessments.

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