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 09 (Year 3 Undergraduate) |
Credits |
10 |
| Home subject area |
Informatics |
Other subject area |
None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/iaml |
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| 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
| Pre-requisites |
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Co-requisites |
|
| Prohibited Combinations |
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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.
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| Additional Costs |
None |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 1, Available to all students (SV1)
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WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | 14:00 - 14:50 | | | | | | Central | Lecture | | 1-11 | | | | 14:00 - 14:50 | |
| First Class |
Week 1, Monday, 14:00 - 14:50, Zone: Central. Lecture Theatre A, David Hume Tower |
|
| Delivery period: 2010/11 Semester 1, Part-year visiting students only (VV1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | 14:00 - 14:50 | | | | | | Central | Lecture | | 1-11 | | | | 14:00 - 14:50 | |
| First Class |
Week 1, Monday, 14:00 - 14:50, Zone: Central. Lecture Theatre A, David Hume Tower |
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. |
| Please see Visiting Student Prospectus website for Visiting Student Assessment information |
Special Arrangements
| Not entered |
Contacts
| Course organiser |
Dr Marcelo Cintra
Tel: (0131 6)50 5118
Email: mc@inf.ed.ac.uk |
Course secretary |
Miss Tamise Totterdell
Tel: 0131 650 9970
Email: t.totterdell@ed.ac.uk |
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copyright 2010 The University of Edinburgh -
1 September 2010 6:10 am
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