THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

Timetable information in the Course Catalogue may be subject to change.

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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Machine Learning Practical (UG) (INFR11223)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis course follows the delivery and assessment of Machine Learning Practical (INFR11132) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11132 instead.
Course description This course follows the delivery and assessment of Machine Learning Practical (INFR11132) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11132 instead.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites It is RECOMMENDED that students also take Applied Machine Learning (INFR11211) OR Machine Learning and Pattern Recognition (INFR11130) OR Accelerated Natural Language Processing (INFR11125) OR Data Analysis and Machine Learning 4 (ELEE10031)
Students MUST also take: Informatics Project Proposal (INFR11147) OR Case Studies in Design Informatics 1 (INFR11094) OR Honours Project (Informatics) (INFR10044) OR MInf Project (Part 2) (INFR11093) OR MInf Project (Part 1) (INFR10051) OR Individual Project in Advanced Natural Language Processing (INFR11192)
Prohibited Combinations Students MUST NOT also be taking Machine Learning Practical (INFR11132)
Other requirements This course follows the delivery and assessment of Machine Learning Practical (INFR11132) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11132 instead.

For Informatics students only (including those on joint degrees), or by special permission of the School.

It is recommended that students have taken a previous course in machine learning (or with significant machine learning content). Those who have not MUST register for one of the co-requisites noted above.

Familiarity with basic mathematics, including algebra and calculus is essential. A reasonable knowledge of computational, logical, geometric and set-theoretic concepts is assumed. Working knowledge of vectors and matrices is also necessary. A basic grasp of probability and partial differentiation, is also required. Students should have programming experience. Programming in a numerical language (Python / Numpy) will be required: previous experience in Python is not mandatory.
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  None
Course Start Full Year
Course Start Date 19/09/2022
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 12, Supervised Practical/Workshop/Studio Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 164 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% Coursework:

Coursework 1 10%
Coursework 2 40%
Coursework 3 formative
Coursework 4 50%
Feedback Summative feedback will be provided through marking of, and comments on, three pieces of assessed coursework. Detailed feedback from each coursework will be provided before the submission deadline of the next piece of coursework. Formative feedback will be provided for the non-assessed coursework, as well being provided via the lab sessions through discussion with the course lecturers, TAs, and demonstrators.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Obtain experience in the design, implementation, training, and evaluation of machine learning systems
  2. Read technical papers, and explain their relevance to the chosen approach
  3. Design and carry out appropriate experiments, and explain the methodology involved
  4. Evaluate the resultant system
  5. Write a scholarly report, suitably structured and with supporting evidence
Reading List
Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, 2016, MIT Press
Michael Nielsen, Neural Networks and Deep Learning, 2016. Online at http://neuralnetworksanddeeplearning.com
Christopher M Bishop, Neural Networks for Pattern Recognition, 1995, Clarendon Press.
Additional Information
Graduate Attributes and Skills Scientific communication
Report writing
Additional Class Delivery Information This course is focused on the implementation and evaluation of machine learning systems, and is lab-based.
Keywordsmachine learning,deep learning,neural networks
Contacts
Course organiserDr Hakan Bilen
Tel: (0131 6)50 2717
Email: hbilen@inf.ed.ac.uk
Course secretaryMrs Helen Tweedale
Tel: (0131 6)50 3827
Email: Helen.Tweedale@ed.ac.uk
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