Undergraduate Course: Introductory Applied Machine Learning (INFR09029)

Course Outline
School	School of Informatics	College	College of Science and Engineering
Course type	Standard	Availability	Available to all students
Credit level (Normal year taken)	SCQF Level 9 (Year 3 Undergraduate)	Credits	10
Home subject area	Informatics	Other subject area	None
Course website	http://www.inf.ed.ac.uk/teaching/courses/iaml	Taught in Gaelic?	No
Course description	Since 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 is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser. Maths requirements: 1 - Probability theory: Discrete and continuous univariate random variables. Expectation, variance. Univariate Gaussian distribution. Joint and conditional distributions. 2 - Linear algebra: Vectors and matrices: definitions, addition. Matrix multiplication, matrix inversion. Eigenvectors, determinants quadratic forms. 3 - Calculus: Functions of several variables. Partial differentiation. Multivariate maxima and minima. 4 - Special functions: Log, exp 5 - Geometry: Basics of lines, planes and hyperplanes. Coordinate geometry of circle, sphere, ellipse, ellipsoid and n-dimensional generalizations. 6 - Entropy: is useful, but will be covered in the lectures. Programming requirements: None.
Additional Costs	None

Information for Visiting Students
Pre-requisites	None
Displayed in Visiting Students Prospectus?	Yes

Course Delivery Information

Delivery period: 2014/15 Semester 1, Available to all students (SV1)			Learn enabled: Yes	Quota: None
Web Timetable	Web Timetable
Course Start Date	15/09/2014
Breakdown of Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 4, Supervised Practical/Workshop/Studio Hours 4, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Additional Notes
Breakdown of Assessment Methods (Further Info)	Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Exam Information
Exam Diet	Paper Name	Hours & Minutes
Main Exam Diet S2 (April/May)		2:00
Resit Exam Diet (August)		2:00

Delivery period: 2014/15 Semester 1, Part-year visiting students only (VV1)			Learn enabled: No	Quota: None
Web Timetable	Web Timetable
Course Start Date	15/09/2014
Breakdown of Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 4, Supervised Practical/Workshop/Studio Hours 4, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Additional Notes
Breakdown of Assessment Methods (Further Info)	Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
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 Assessment 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
None

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
Keywords	Not entered