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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 (INFR11119)

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 Credits10 ECTS Credits5
Summary***PLEASE NOTE - this course has been replaced by Machine Learning Practical INFR11132 (20 credit course) from 2016/17.***

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.
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 8 hours of 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 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
Not being delivered
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 secretaryMiss Claire Edminson
Tel: (0131 6)51 4164
Email: C.Edminson@ed.ac.uk
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