Postgraduate Course: Machine Learning & Pattern Recognition (Level 11) (INFR11073)
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 11 (Postgraduate) 
Credits  10 
Home subject area  Informatics 
Other subject area  None 
Course website 
http://www.inf.ed.ac.uk/teaching/courses/mlpr 
Taught in Gaelic?  No 
Course description  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 realworld 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 (not applicable to Visiting Students)
Prerequisites 

Corequisites  
Prohibited Combinations  Students MUST NOT also be taking
Machine Learning & Pattern Recognition (Level 10) (INFR10036)

Other requirements  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 settheoretic concepts is assumed. Working knowledge of vectors and matrices is also necessary. A basic grasp of probability and partial differentiation, is strongly recommended. 
Additional Costs  None 
Information for Visiting Students
Prerequisites  None 
Displayed in Visiting Students Prospectus?  No 
Course Delivery Information

Delivery period: 2011/12 Semester 2, Available to all students (SV1)

WebCT enabled: No 
Quota: None 
Location 
Activity 
Description 
Weeks 
Monday 
Tuesday 
Wednesday 
Thursday 
Friday 
Central  Lecture   111   11:10  12:00     Central  Lecture   111      11:10  12:00 
First Class 
Week 1, Tuesday, 11:10  12:00, Zone: Central. George Sq 07 F21 
Exam Information 
Exam Diet 
Paper Name 
Hours:Minutes 


Main Exam Diet S2 (April/May)   2:00   
Summary of Intended Learning Outcomes
1  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.
2  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.
3  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.
4  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.
5  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.
6  Breadth of Thinking  Learning outcome: Given a complex problem, students will be able to: (a) identify subproblems that are amenable to solution using Machine Learning techniques, (b) provide solutions to those subproblems, and evaluation of the solutions. 
Assessment Information
Written Examination 80
Assessed Assignments 20
Oral Presentations 0
Assessment
There will be two assignments for the course, one for each half of the course contents. This will involve practical hands on data analysis as well as questions about the ideas on the course. The level 11 course will test for the additional learning outcome.
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 
* Data and Models: Introducing Data, Probability and Bayesian Presumptions.
* Simple Distributions, Maximum Likelihood and Bayesian Estimation.
* Bayesian Sets Example
* The Exponential Family
* Multivariate Gaussians, PCA and PPCA. Bayesian Gaussian
* Linear Parameter Models, Bayesian Regression
* Logistic Regression and Neural Networks
* Optimisation
* Approximate Methods: Laplace, Variational Methods, Sampling.
* Naïve Bayes, Class Conditional Gaussians, Gaussian Mixtures and EM.
* Gaussian Processes and Kernel Methods
* Bayesian Decision Theory.
Relevant QAA Computing Curriculum Sections: Artificial Intelligence, HumanComputer Interaction (HCI), Intelligent Information Systems Technologies, Natural Language Computing, Simulation and Modelling, Theoretical Computing. 
Transferable skills 
Not entered 
Reading list 
* Self contained course notes (Barber 2007)
* C.M. Bishop (2006) Pattern Recognition and Machine Learning. Springer.
* Duda Hart and Stork. (2001). Pattern Classification. Wiley 
Study Abroad 
Not entered 
Study Pattern 
Lectures 20
Tutorials 8
Timetabled Laboratories 0
Nontimetabled assessed assignments 22
Private Study/Other 50
Total 100 
Keywords  Not entered 
Contacts
Course organiser  Dr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk 
Course secretary  Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk 

