Undergraduate Course: Machine Learning Theory (UG) (INFR11224)
This course will be closed from 31 July 2025
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Following the closure of this course, a suggested replacement for students to consider is: Advanced Topics in Machine Learning (UG) INFR11289.
This course follows the delivery and assessment of Machine Learning Theory (INFR11202) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11202 instead. |
| Course description |
This course follows the delivery and assessment of Machine Learning Theory (INFR11202) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11202 instead.
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Information for Visiting Students
| Pre-requisites | Students should be confident in standard machine learning ideas: training and test sets, classification, regression, clustering; standard machine learning methods: support vector machines, linear regression, k-means etc. Good understanding of probability and probabilistic arguments is necessary. Experience of hands on data analysis and use of machine learning can be beneficial, but is not necessary.
This is a fourth-year honours level course; students are expected to have an academic profile equivalent to the first three years of this degree programme. Study equivalent to the following University of Edinburgh courses is recommended: Introductory Applied Machine Learning (INFR10069) OR Applied Machine Learning (INFR11211) OR Machine Learning and Pattern Recognition (INFR11130) OR Machine Learning (INFR10086) OR Data Analysis and Machine Learning 4 (ELEE10031) |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- interpret and explain rigorous statements about properties of machine learning methods
- evaluate properties of learning models through proofs and examples
- relate, compare, and contrast the implications of various qualities of machine learning models covered in the course
- formulate precise mathematical requirements corresponding to desired properties in real learning problems, and explain their decisions
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Reading List
| 'Understanding Machine Learning: From Theory to Algorithms', by Shai Ben-David and Shai Shalev-Schwartz |
Additional Information
| Course URL |
https://opencourse.inf.ed.ac.uk/mlt |
| Graduate Attributes and Skills |
Problem solving
Critical / analytical thinking
Independent learning
Written communication |
| Keywords | machine learning,data science,algorithms,theory |
Contacts
| Course organiser | Dr Rik Sarkar
Tel: (0131 6)50 4444
Email: Rik.Sarkar@ed.ac.uk |
Course secretary | Miss Kerry Fernie
Tel: (0131 6)50 5194
Email: kerry.fernie@ed.ac.uk |
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