Introductory Applied Machine Learning (VS1) (U04285)

? Credit Points : 10 ? SCQF Level : 9 ? Acronym : INF-3-IAML-V

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

? This course is only available to part year visiting students.

? This course is a variant of the following course : U04180

? Pre-requisites : Familiarity with basic mathematics including calculus, linear algebra and probablility, as would be obtained, for example in Mathematics for Informatics 1-4. A reasonable level of familiarity with computational concepts.

Subject Areas

Home subject area

Artificial Intelligence, (School of Informatics, Schedule O)

Other subject areas

Computational Linguistics, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : 3rd year

? Delivery Period : Semester 1 (Blocks 1-2)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
25/09/2008	14:00	14:50	Lecture Theatre 270, Old College	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	14:00	14:50	Central
Lecture	Thursday	14:00	14:50	Central

Summary of Intended Learning Outcomes

1. Explain the scope, goals and limits of machine learning, and the main subareas 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.