Undergraduate Course: Introductory Applied Machine Learning (INFR10063)
Course Outline
School  School of Informatics 
College  College of Science and Engineering 
Credit level (Normal year taken)  SCQF Level 10 (Year 3 Undergraduate) 
Availability  Available to all students 
SCQF Credits  10 
ECTS Credits  5 
Summary  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. 
Course description 
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 modern machine learning programming environment]
Relevant QAA Computing Curriculum Sections: Artificial Intelligence, HumanComputer Interaction (HCI), Intelligent Information Systems Technologies, Natural Language Computing, Simulation and Modelling, Theoretical Computing

Entry Requirements (not applicable to Visiting Students)
Prerequisites 

Corequisites  
Prohibited Combinations  
Other requirements  This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
Maths requirements:
1  Probability theory: Discrete and continuous univariate random variables. Expectation, variance. Univariate Gaussian distribution. Joint and conditional distributions.
2  Linear algebra: Vectors and matrices: definitions, addition. Matrix multiplication, matrix inversion. Eigenvectors, determinants quadratic forms.
3  Calculus: Functions of several variables. Partial differentiation. Multivariate maxima and minima.
4  Special functions: Log, exp
5  Geometry: Basics of lines, planes and hyperplanes. Coordinate geometry of circle, sphere, ellipse, ellipsoid and ndimensional generalizations.
6  Entropy: is useful, but will be covered in the lectures.
Programming requirements: Students should be able to program in a modern objectoriented language such as Python. 
Information for Visiting Students
Prerequisites  Visiting students are required to have comparable background to that
assumed by the course prerequisites listed in the Degree Regulations &
Programmes of Study. If in doubt, consult the course lecturer. 
High Demand Course? 
Yes 
Course Delivery Information

Academic year 2015/16, Available to all students (SV1)

Quota: None 
Course Start 
Semester 1 
Timetable 
Timetable 
Learning and Teaching activities (Further Info) 
Total Hours:
100
(
Lecture Hours 20,
Seminar/Tutorial Hours 4,
Supervised Practical/Workshop/Studio Hours 4,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
68 )

Assessment (Further Info) 
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %

Additional Information (Assessment) 
There will be at least 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.
You should expect to spend approximately 22 hours on the coursework for this course.
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.

Feedback 
Not entered 
Exam Information 
Exam Diet 
Paper Name 
Hours & Minutes 

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

Academic year 2015/16, Partyear visiting students only (VV1)

Quota: None 
Course Start 
Semester 1 
Timetable 
Timetable 
Learning and Teaching activities (Further Info) 
Total Hours:
100
(
Lecture Hours 20,
Seminar/Tutorial Hours 4,
Supervised Practical/Workshop/Studio Hours 4,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
68 )

Assessment (Further Info) 
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %

Additional Information (Assessment) 
There will be at least 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.
You should expect to spend approximately 22 hours on the coursework for this course.
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.

Feedback 
Not entered 
Exam Information 
Exam Diet 
Paper Name 
Hours & Minutes 

Main Exam Diet S1 (December)   2:00  
Learning Outcomes
On completion of this course, the student will be able to:
 Explain the scope, goals and limits of machine learning, and the main subareas of the field.
 Describe the various techniques covered in the syllabus and where they fit within the structure of the discipline.
 Students should be able to critically compare, contrast and evaluate the different ML techniques in terms of their applicability to different Machine Learning problems.
 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.
 Given appropriate data students should be able to use a systematic approach to conducting experimental investigations and assessing scientific hypotheses.

Reading List
Data Mining: Practical Machine Learning Tools and Techniques (Second Edition) I. H. Witten and E. Frank, Morgan Kaufmann, 2005. ISBN 0120884070 
Contacts
Course organiser  Dr Nigel Goddard
Tel: (0131 6)51 3091
Email: Nigel.Goddard@ed.ac.uk 
Course secretary  Miss Beth Muir
Tel: (0131 6)51 7607
Email: beth.muir@ed.ac.uk 

