PHYS51915: Core Ia: Introduction to Machine Learning and Statistics

• A UK first or upper second class honours degree (BSc) or equivalent in Physics or a subject with basic physics courses OR in Computer Science OR in Mathematics OR in any natural sciences with a strong quantitative element. Programming knowledge in at least one programming language and commitment to learning C and Python independently if not known before.

• PHYSPGNEW03 Core Ib: Introduction to Scientific and High-Performance Computing

• None

• Provide basic knowledge and critical understanding of paradigms, fundamental ideas and methods of data analysis and statistics
• Provide basic knowledge and critical understanding of paradigms, fundamental ideas and methods of machine learning

• Introduction to statistics and data analysis
• Introduction to Machine Learning, classification and regression.

Subject-specific Knowledge:

• understanding and critical reflection of fundamental ideas and techniques in the application of data analysis and statistics to scientific data.
• understanding and critical reflection of fundamental ideas and techniques in the application of machine learning to scientific data.

Subject-specific Skills:

• competent and education selection and application of programming languages, algorithms and computing tools for specific problems.

Key Skills:

• Familiarity with basic paradigms and modern concepts underlying data analysis

• Teaching will be by lectures and workshops.
• The lectures provide the means to give a concise, focused presentation of the subject matter of the module.
• When appropriate, the lectures will also be supported by the distribution of written material, or by information and relevant links on DUO
• Regular problem exercises and workshops will give students the chance to develop their theoretical understanding and problem solving skills
• Students will be able to obtain further help in their studies by approaching their lecturers, either after lectures or at other mutually convenient times
• Student performance will be summatively assessed through coursework
• The formative coursework provides opportunities for feedback, for students to gauge their progress and for staff to monitor progress throughout the duration of the module.

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