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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2017/2018

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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Machine Learning Practical (INFR11132)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course is focused on the implementation and evaluation of machine learning systems, and is lab-based. Students who do this course will obtain experience in the design, implementation, training, and evaluation of machine learning systems.

This 20 credit course replaces INFR11119 Machine Learning Practical (10 credits).
Course description The course will cover practical aspects of machine learning, and will focus on practical and experimental issues for a particular topic. Example topics on which the course could focus include:
* Artificial (deep) neural networks
* Reinforcement learning
* Gaussian processes

The course syllabus will thus be based on the particular focus for that year. For example, if the course focuses on artificial neural networks then following items would be covered:
* Feed-forward network architectures
* Optimisation of neural networks (stochastic gradient descent)
* Regularization
* Neural networks for classification
* Neural networks for regression
* Possible additional topics: Convolutional Neural Networks, Autoencoders, Restricted Boltzamnn Machines, Recurrent Neural Networks

MLP will be coursework-based, with lectures to support the additional material required to carry out the practical. Students who do this course will have experience in the design, implementation, training, and evaluation of machine learning systems.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Machine Learning Practical (INFR11119)
Other requirements 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.
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2017/18, Available to all students (SV1) Quota:  None
Course Start Full Year
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:
Assessment 1 - due week 6, Semester 1 - 10%
Assessment 2 - due week 10, Semester 1 - 25%
Assessment 3 - due week 3, Semester 2 - 25%
Assessment 4 - due week 8, Semester 2 - 40%

Feedback Summative feedback will provided through marking of, and comments on, the assessed practicals. Detailed feedback from the first practical will be provided before the second practical deadline. All students will also be invited to sign-up to individual meetings to discuss the practical assessment. Formative feedback will provided via the lab sessions through discussion with the course lecturer and TA.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. This course is focused on the implementation and evaluation of machine learning systems, and is lab-based. Students who do this course will 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
Bishop: Pattern Recognition and Machine Learning;
Murphy: Machine Learning - A Probabilistic Perspective.
Also specific material related to the course focus.
Additional Information
Course URL http://www.inf.ed.ac.uk/teaching/courses/mlp
Graduate Attributes and Skills Scientific communication / report writing.
KeywordsMachine learning,Pattern recognition,Data Science
Contacts
Course organiserProf Stephen Renals
Tel: (0131 6)50 4589
Email: s.renals@ed.ac.uk
Course secretaryMr Gregor Hall
Tel: (0131 6)50 5194
Email: gregor.hall@ed.ac.uk
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