Machine Learning & Pattern Recognition (Level 11) (P02752)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-MLPR-5

Both the study of Artificial Intelligence - understanding how to build learning machines - and the business of developing tools to analyse the numerous increasing data sources involves developing a systematic understanding of how we can learn from data. A principled approach to this problem is critical given the wide differences in the places these methods need to be used.

This course is a foundational course for anyone pursuing machine learning, or interested in the intelligent utilisation of machine learning methods. The primary aim of the course is enable the student to think coherently and confidently about machine learning problems, and present 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.

This course avoids the potential pitfalls of simply presenting a set of machine learning tools as if they were an end in themselves, but follows the basic principles of machine learning methods in showing how the different tools are developed, how they are related, how they should be deployed, and how they are used in practice. The course presents a number of methods in machine learning that are increasingly used, including Bayesian methods, and Gaussian processes.

This course is identical to the level 10 version except for an additional learning outcome, and a consequential difference in assessment.

Entry Requirements

? Pre-requisites : For Informatics PG and final year MInf students only, or by special permission of the School. 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 strongly recommended.

? Prohibited combinations : Machine Learning & Pattern Recognition (Level 10)

Subject Areas

Home subject area

Artificial Intelligence, (School of Informatics, Schedule O)

Other subject areas

Life Science Computing, (School of Informatics, Schedule O)

Cognitive Science, (School of Informatics, Schedule O)

Computer Science, (School of Informatics, Schedule O)

Computational Linguistics, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

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

First Class Information

Date	Start	End	Room	Area	Additional Information
13/01/2009	11:10	12:00			7 George Square F21

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	11:10	12:00	Central
Lecture	Friday	11:10	12:00	Central

Summary of Intended Learning Outcomes

Way of thinking - the course introduces an approach to thinking about machine learning problems. Learning Outcome: The students will be able to describe why a particular model is appropriate in a given situations, formulate the model and use it appropriately.
A strong foundation - the course will provide students with the core techniques and methods needed to use machine learning in any area. Learning Outcome: The student will be able to analytically demonstrate how different models and different algorithms are related to one another.

Practical capability - the course will provide students with the theoretical background needed to assess good practice, along with the practical experience. Learning Outcome: Students will be able to implement a set of practical methods, given example algorithms in MATLAB, and be able to program solutions to some given real world machine learning problems, using the toolbox of practical methods presented in the lectures.

Thoroughness - students will leave the course with a deep understanding of machine learning and its aims and limitations. Learning Outcome: Given a particular situation, students will be able be able to justify why a given model is appropriate for the situation or why it is not appropriate. Students will be able to developing an appropriate algorithm from a given model, and demonstrate the use of that method.

Coherence - the course provide a unifying coherent view on machine learning. Learning Outcome: students will be able to design and compare machine learning methods, and discuss how different methods relate to one another and will be able to develop new and appropriate machine learning methods appropriate for particular problems.

Breadth of Thinking - Learning outcome: Given a complex problem, students will be able to: (a) identify sub-problems that are amenable to solution using Machine Learning techniques, (b) provide solutions to those sub-problems, and evaluation of the solutions.