Durham University
Programme and Module Handbook

Postgraduate Programme and Module Handbook 2024-2025

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 2024/2025 Module Cap None.
Tied to G5K609

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