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.
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Information for Visiting Students
Pre-requisites | As above. |
High Demand Course? |
Yes |
Course Delivery Information
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Academic year 2024/25, Available to all students (SV1)
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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 )
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Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
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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 |
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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
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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 Yesica Marco Azorin
Tel: (0131 6)50 5194
Email: ymarcoa@ed.ac.uk |
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