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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Introductory Applied Machine Learning (INFR09029)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 9 (Year 3 Undergraduate) Credits10
Home subject areaInformatics Other subject areaNone
Course website Taught in Gaelic?No
Course descriptionSince the early days of AI, researchers have been interested in making computers learn, rather than simply programming them to do tasks. This is the field of machine learning. The main area that will be discussed is supervised learning, which is concerned with learning to predict an output, given inputs. A second area of study is unsupervised learning, where we wish to discover the structure in a set of patterns; there is no output "teacher signal".

The primary aim of the course is to provide the student with a set of practical tools that can be applied to solve real-world problems in machine learning, coupled with an appropriate, principled approach to formulating a solution.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements This course has the following mathematics prerequisites:

1 - Probability theory: Discrete and continuous univariate random variables. Expectation, variance. Univariate Gaussian distribution. Joint and conditional distributions. (At the level taught in MfI 1&4)

2 - Linear algebra: Vectors and matrices: definitions, addition. Matrix multiplication, matrix inversion. Eigenvectors, determinants quadratic forms. (At the level taught in MfI 2&3).

3 - Calculus: Functions of several variables. Partial differentiation. Multivariate maxima and minima. (At the level taught in MfI 1&2)

4 - Special functions: Log, exp are fundamental.(At the level taught in MfI 1)

5 - Geometry: Basics of lines, planes and hyperplanes. Coordinate geometry of circle, sphere, ellipse, ellipsoid and n-dimensional generalizations. (At level taught in MfI 1&4)

6 - Entropy: (as covered in MfI 1) is useful, but will be covered in the lectures.
Additional Costs None
Information for Visiting Students
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2011/12 Semester 1, Available to all students (SV1) WebCT enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 14:00 - 14:50
CentralLecture1-11 14:00 - 14:50
First Class Week 1, Monday, 14:00 - 14:50, Zone: Central. AT LT1
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S2 (April/May)2:00
Resit Exam Diet (August)2:00
Delivery period: 2011/12 Semester 1, Part-year visiting students only (VV1) WebCT enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 14:00 - 14:50
CentralLecture1-11 14:00 - 14:50
First Class Week 1, Monday, 14:00 - 14:50, Zone: Central. AT LT1
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S1 (December)2:00
Summary of Intended Learning Outcomes
1 - Explain the scope, goals and limits of machine learning, and the main sub-areas of the field.
2 - Describe the various techniques covered in the syllabus and where they fit within the structure of the discipline.
3 - Students should be able to critically compare, contrast and evaluate the different ML techniques in terms of their applicability to different Machine Learning problems.
4 - Given a data set and problem students should be able to use appropriate software to apply these techniques to the data set to solve the problem.
5 - Given appropriate data students should be able to use a systematic approach to conducting experimental investigations and assessing scientific hypotheses.
Assessment Information
Written Examination 75
Assessed Assignments 25
Oral Presentations 0

There would be one assignment, where a number machine learning methods would be applied to a dataset. There could be more than one dataset available to cater for different tastes.

If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Special Arrangements
Additional Information
Academic description Not entered
Syllabus Introduction to Machine Learning and its Goals. Introduction to Data and Models. Memory based methods. Decision Trees. Error functions, Minimizing Error. Regression, Logistic Regression, Neural Networks. Margin Based Methods: Perceptron, Support Vector Machines. Na´ve Bayes. Dimensionality Reduction. Clustering: K-means, Simple Gaussian Mixture Models, Hierarchical Clustering. Boosting Approaches. Model Averaging, Mixtures of Experts. Evaluation of Performance.

[We will also use a suite of machine learning software, e.g. WEKA.]

Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Human-Computer Interaction (HCI), Intelligent Information Systems Technologies, Natural Language Computing, Simulation and Modelling, Theoretical Computing
Transferable skills Not entered
Reading list Data Mining: Practical Machine Learning Tools and Techniques (Second Edition) I. H. Witten and E. Frank, Morgan Kaufmann, 2005. ISBN 0-12-088407-0
Study Abroad Not entered
Study Pattern Lectures 20
Tutorials 4
Timetabled Laboratories 4
Non-timetabled assessed assignments 22
Private Study/Other 50
Total 100
KeywordsNot entered
Course organiserDr Nigel Goddard
Tel: (0131 6)51 3091
Course secretaryMiss Tamise Totterdell
Tel: 0131 650 9970
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