Undergraduate Course: Machine Learning Theory (INFR11202)
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 is an introduction to the theory of learning algorithms and their properties that are relevant to the widespread use of machine learning. The course starts with the standard mathematical concepts in theoretical ML. It then covers classical analytic results about accuracy, confidence, sample complexity and model complexity. Standard learning/optimisation algorithms are described in this context. In modern research areas such as trustworthy machine learning, several properties including privacy, fairness and interpretability are considered vital for widespread reliable use of machine learning. These topics are discussed in a mathematical perspective.
The course aims to provide a firm foundation in reading and understanding mathematical ideas so that students are equipped to follow the latest developments and research, and to interpret relevant properties and tradeoffs. Throughout, the course will take the approach of precise mathematical definition and analysis, coupled with easy examples and intuition to aid understanding. 
Course description 
The following is an indicative list of topics in the course:
1. Notations, terminology and formal models.
2. Learning theory: Empirical risk minimisation and sampling complexity. Probably approximately correct (PAC) guarantees.
3. Complexity of learning models (e.g. VC dimension) and biascomplexity tradeoff.
4. Optimization algorithms. Regression, SVM, Stochastic gradient descent and its variants.
5. Regularization, convexity, stability, Lipschitzness and other properties
6. Statistical Privacy
7. Mechanisms for privacy preserving machine learning. Differentially private stochastic gradient descent.
8. Interpretable machine learning. (E.g. Feature importance)
9. Fairness.
The topics will be discussed with reference to standard machine learning techniques, and examples of realistic problems. Our approach will include precise definitions and analysis as well as examples and intuitive explanations. The relevance and domain of applicability of the various concepts will be discussed.
Tutorials and problem sets will be available to help understanding and exploration of the subject.

Information for Visiting Students
Prerequisites  As above. 
High Demand Course? 
Yes 
Course Delivery Information

Academic year 2023/24, 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) 
Written 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 (INFR11202)  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
 telate, 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 BenDavid and Shai ShalevSchwartz 
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  Miss Yesica Marco Azorin
Tel: (0131 6)505113
Email: ymarcoa@ed.ac.uk 

