Offered to students admitted to Year 1 in ALL
Major/Minor ALL
Course Type
Offer in 2023 - 2024 Y N
Course Code PHYS3151
Date2023/09/24 20:47
Enquiry for Course Details
PHYS3151 Machine learning in physics (6 credits) Academic Year 2023
Offering Department Physics Quota ---
Course Co-ordinator Dr Z Y Meng, Physics < zymeng@hku.hk >
Teachers Involved (Dr Z Y Meng,Physics)
Course Objectives Machine learning is a technique that enables computers to learn without being explicitly "programmed''. It is an essential part of big data science and has been widely used in different fields of physics. This course introduces the basics of machine learning, from key concepts to practical algorithms, with a focus on real-world applications in physics.  It is an elective course for the computational physics theme.  This is also an essential course for those who plan to apply machine learning techniques in their postgraduate studies such as condensed matter physics and astrophysics or in their future work.
Course Contents & Topics Machine learning software packages in Python, Supervised and Unsupervised learning, Regression, Classification, Principal component analysis, Singular value decomposition, Support vector machines, Clustering, K-Nearest Neighbors, Neural Networks, Deep Learning, Application of machine learning in physics research with examples drawing from fields such as condensed matter physics, quantum material, astrophysics, particle physics and complex systems.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 demonstrate knowledge in essential methods and techniques for machine learning and its application in physics
CLO 2 apply the techniques of machine learning in data analysis
CLO 3 use Python machine learning packages to solve simple problems
CLO 4 use of effective written and verbal communication skills through oral presentation
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in MATH2014 or MATH2101 or MATH2211 or PHYS2155 or PHYS2160.
Working knowledge of Python is needed (please talk to the course instructor in case of doubt).
Course Status with Related Major/Minor /Professional Core 2023 Major in Physics ( Disciplinary Elective )
2023 Major in Physics (Intensive) ( Disciplinary Elective )
2023 Minor in Physics ( Disciplinary Elective )
2022 Major in Physics ( Disciplinary Elective )
2022 Major in Physics (Intensive) ( Disciplinary Elective )
2022 Minor in Physics ( Disciplinary Elective )
2021 Major in Physics ( Disciplinary Elective )
2021 Major in Physics (Intensive) ( Disciplinary Elective )
2021 Minor in Physics ( Disciplinary Elective )
2020 Major in Physics ( Disciplinary Elective )
2020 Major in Physics (Intensive) ( Disciplinary Elective )
2020 Minor in Physics ( Disciplinary Elective )
2019 Major in Physics ( Disciplinary Elective )
2019 Major in Physics (Intensive) ( Disciplinary Elective )
2019 Minor in Physics ( Disciplinary Elective )
Course to PLO Mapping 2023 Major in Physics < PLO 1,2,3,4 >
2023 Major in Physics (Intensive) < PLO 1,2,3,4 >
2022 Major in Physics < PLO 1,2,3,4 >
2022 Major in Physics (Intensive) < PLO 1,2,3,4 >
2021 Major in Physics < PLO 1,2,3,4 >
2021 Major in Physics (Intensive) < PLO 1,2,3,4 >
2020 Major in Physics < PLO 1,2,3,4 >
2020 Major in Physics (Intensive) < PLO 1,2,3,4 >
2019 Major in Physics < PLO 1,2,3,4 >
2019 Major in Physics (Intensive) < PLO 1,2,3,4 >
Offer in 2023 - 2024 Y        2nd sem    Examination May     
Offer in 2024 - 2025 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills. Apply highly effective lab skills and techniques. Critical use of data and results to draw appropriate and insightful conclusions.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills. Apply effective lab skills and techniques. Correct use of data of results to draw appropriate conclusions.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills. Apply moderately effective lab skills and techniques. Mostly correct but some erroneous use of data and results to draw appropriate conclusions.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills. Apply partially effective lab skills and techniques. Limited ability to use data and results to draw appropriate conclusions.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective. Apply minimally effective or ineffective lab skills and techniques. Misuse of data and results and/or unable to draw appropriate conclusions.
Communication-intensive Course N
Course Type Lecture with laboratory component course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Laboratory 12
Lectures 36
Tutorials 8
Reading / Self study 80
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments 30 CLO 1,2,3,4
Examination 2-hour written exam 30 CLO 1,2,3
Presentation 20 CLO 1,2,3,4
Project report 20 CLO 1,2,3,4
Required/recommended reading
and online materials		 Lecture notes provided by Course Coordinator
E. Alpaydin, Introduction to Machine Learning, 3rd ed., MIT Press (2014)
T. Hastie, R. Tibshirani, & J. Friedman, The Elements of Statistical Learning, 2nd ed., Springer (2016)
S. Raschka, Python Machine Learning, 2nd ed., Packt Publishing (2017)
Course Website http://moodle.hku.hk
Additional Course Information NIL
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