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DRPS : Course Catalogue : School of Physics and Astronomy : Postgraduate (School of Physics and Astronomy)

Postgraduate Course: Data Analysis and Machine Learning (PGPH11105)

Course Outline
SchoolSchool of Physics and Astronomy CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis course develops core skills related to data analysis and statistical interpretation. After a recap of modern programmingtools (NumPy, SciPy, matplotlib) and statistical methods, the students are introduced to maximum likelihood fits, systematic effects and hypothesis testing. Advanced techniques such as multivariate methods and machine learning will be introduced. The simulation of experimental data will also be covered. At the end of the course data analysis techniques will be applied to current experiments.
Course description A course split over semesters 1 and 2 that builds core skills related to data analysis and statistical methods. It will be taught by a combination of lectures and laboratory workshops in which students will carry out practical programming exercises. A significant amount of the course assessment will be by project work. In the first part of the course scientific programming in C++ and Python will be introduced. An introduction to statistical concepts will also be given. This will be followed by a detailed discussion of maximum likelihood fitting methods, hypothesis testing and limit setting. Advanced multivariate analysis techniques will be covered, including machine learning. Important practical considerations such as the treatment of systematic uncertainties, and the simulation of experimental data will be discussed. In the last part of the course data analysis techniques will be applied to current experiments.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Modern Physics (PHYS08045) AND Fourier Analysis and Statistics (PHYS09055) OR Probability (MATH08066) AND Computer Modelling (PHYS09057) OR Numerical Recipes (PHYS10090)
Co-requisites It is RECOMMENDED that students also take Detectors in Particle & Nuclear Physics (PGPH11104)
Prohibited Combinations Other requirements Students must have programming experience and feel comfortable using python (mandatory) and C++, NumPy, SciPy, and Jupyter notebooks (desirable).
PGT students have priority for this course, after which MPhys Y5 students will be accommodated.
Course Delivery Information
Academic year 2024/25, Not available to visiting students (SS1) Quota:  0
Course Start Full Year
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 60, Feedback/Feedforward Hours 4, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 110 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 48% Continuous Assessment
52% Project Work
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand the statistical basis for data analysis in particle and nuclear physics.
  2. Perform complex maximum likelihood fits.
  3. Demonstrate usage of Monte Carlo methods and simulation techniques.
  4. Apply advanced techniques such as multivariate analysis and machine learning to solve problems in data analysis.
  5. Critically judge how data analysis techniques are used in current research.
Reading List
Additional Information
Graduate Attributes and Skills Demonstrate a critical understanding of the principal theories,concepts and principles of data analysis. Apply knowledge, skills and understanding to a range of standard and specialised research techniques in data analysis. Critically review, consolidate and extend knowledge, skills, practices and thinking in the subject. Undertake critical evaluations of a wide range of numerical andgraphical data. Communicate with peers, more senior colleagues and specialists via written reports. Exercise substantial autonomy and initiative.
KeywordsDAML,machine learning,Data analysis,Statistical methods
Course organiserDr Christos Leonidopoulos
Course secretaryMr Kieran Brodie
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