Postgraduate Programme and Module Handbook 2022-2023 (archived)
Module PHYS51915: Core Ia: Introduction to Machine Learning and Statistics
Department: Physics
PHYS51915: Core Ia: Introduction to Machine Learning and Statistics
| Type | Tied | Level | 5 | Credits | 15 | Availability | Available in 2022/23 | Module Cap | None. |
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| Tied to | G5K609 |
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Prerequisites
- 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.
Corequisites
- PHYSPGNEW03 Core Ib: Introduction to Scientific and High-Performance Computing
Excluded Combination of Modules
- None
Aims
- 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
Content
- Introduction to statistics and data analysis
- Introduction to Machine Learning, classification and regression.
Learning Outcomes
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
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- 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.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures in Introduction to Statistics and Data Analysis | 8 | 2 per week | 1 hour | 8 | |
| Practical Classes in Introduction to Statistics and Data Analysis | 8 | 2 per week | 1 hour | 8 | |
| Lectures in Introduction to Machine Learning | 8 | 2 per week | 1 hour | 8 | |
| Practical Classes in Introduction to Machine Learning | 8 | 2 per week | 1 hour | 8 | |
| Self-study | 118 | ||||
| Total | 150 |
Summative Assessment
| Component: Coursework | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Statistics and Machine Learning Coursework | 50% | ||
| Data Analysis Coursework | 50% | ||
Formative Assessment:
Feedback on coursework
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University