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 realworld problems in machine learning, coupled with an appropriate, principled approach to formulating a solution. 
Entry Requirements (not applicable to Visiting Students)
Prerequisites 

Corequisites  
Prohibited Combinations  
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 ndimensional generalizations. (At level taught in MfI 1&4)
6  Entropy: (as covered in MfI 1) is useful, but will be covered in the lectures. 
Additional Costs  None 
Information for Visiting Students
Prerequisites  None 
Displayed in Visiting Students Prospectus?  Yes 
Course Delivery Information

Delivery period: 2012/13 Semester 1, Available to all students (SV1)

Learn enabled: Yes 
Quota: None 
Location 
Activity 
Description 
Weeks 
Monday 
Tuesday 
Wednesday 
Thursday 
Friday 
Central  Lecture   111  14:00  14:50      Central  Lecture   111     14:00  14:50  
First Class 
Week 1, Monday, 14:10  15:00, Zone: Central. LT1 Appleton Tower 
Exam Information 
Exam Diet 
Paper Name 
Hours:Minutes 


Main Exam Diet S2 (April/May)   2:00    Resit Exam Diet (August)   2:00   

Delivery period: 2012/13 Semester 1, Partyear visiting students only (VV1)

Learn enabled: No 
Quota: None 
Location 
Activity 
Description 
Weeks 
Monday 
Tuesday 
Wednesday 
Thursday 
Friday 
Central  Lecture   111  14:00  14:50      Central  Lecture   111     14:00  14:50  
First Class 
Week 1, Monday, 14:10  15:00, Zone: Central. LT1 Appleton Tower 
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 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. 
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. Naïve Bayes. Dimensionality Reduction. Clustering: Kmeans, 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, HumanComputer 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 0120884070 
Study Abroad 
Not entered 
Study Pattern 
Lectures 20
Tutorials 4
Timetabled Laboratories 4
Nontimetabled assessed assignments 22
Private Study/Other 50
Total 100 
Keywords  Not entered 
Contacts
Course organiser  Mr Vijayanand Nagarajan
Tel: (0131 6)51 3440
Email: vijay.nagarajan@ed.ac.uk 
Course secretary  Mrs Victoria Swann
Tel: (0131 6)51 7607
Email: Vicky.Swann@ed.ac.uk 

