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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Machine Learning Theory (UG) (INFR11224)

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	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.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites	It is RECOMMENDED that students have passed 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)	Co-requisites
Prohibited Combinations	Students MUST NOT also be taking Machine Learning Theory (INFR11202)	Other requirements	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. 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.

Information for Visiting Students
Pre-requisites	As above.
High Demand Course?	Yes

Course Delivery Information

Academic year 2022/23, Available to all students (SV1)		Quota: None
Course Start	Semester 2
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 18, Seminar/Tutorial Hours 3, Feedback/Feedforward Hours 1, Summative Assessment Hours 2, Revision Session Hours 1, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 73 )
Assessment (Further Info)	Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Additional Information (Assessment)	Exam 70% Coursework 30%
Feedback	Students will receive feedback in the forms of assessment of coursework, tutorials, and problem sets.
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S2 (April/May)	Machine Learning Theory (UG) (INFR11224)		2:00

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

Reading List
'Understanding Machine Learning: From Theory to Algorithms', by Shai Ben-David and Shai Shalev-Schwartz

Additional Information
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	Mrs Helen Tweedale Tel: (0131 6)50 2692 Email: Helen.Tweedale@ed.ac.uk

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