Postgraduate Course: Machine Learning Methods for Data Science (INFR11135)
Course Outline
School  School of Informatics 
College  College of Science and Engineering 
Credit level (Normal year taken)  SCQF Level 11 (Postgraduate) 
Course type  Online Distance Learning 
Availability  Not available to visiting students 
SCQF Credits  20 
ECTS Credits  10 
Summary  Organisations seek to make better decisions by examining their data with an aim to discovering and/or drawing conclusions about the information contained within.
This course is about the principled application of machine learning techniques to extracting information from data. 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, i.e. there is no output "teacher signal".
The primary aim 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. 
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.
 Evaluation of Performance.
We will also use a modern machine learning programming environment.

Entry Requirements (not applicable to Visiting Students)
Prerequisites 
It is RECOMMENDED that students have passed
Practical Introduction to Data Science (PGPH11092)

Corequisites  
Prohibited Combinations  
Other requirements  For distance learning students only.
Students should be familiar with programming in a modern objectoriented language, ideally Python which is the course language. 
Course Delivery Information
Not being delivered 
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
The course textbook is Data Mining: Practical Machine Learning Tools and Techniques (Third Edition, 2011) by Ian H. Witten and Eibe Frank.
Other good books:
 Pattern Recognition and Machine Learning by C. Bishop
 Elements of Statistical Learning by Hastie, Tibshirani and Friedman
 Bayesian Reasoning and Machine Learning by D. Barber
 Machine Learning by T. Mitchell
 Reinforcement Learning by R. Sutton and A. Barto
 A Few Useful Things to Know about Machine Learning by P. Domingos 
Contacts
Course organiser  Dr Nigel Goddard
Tel: (0131 6)51 3091
Email: Nigel.Goddard@ed.ac.uk 
Course secretary  Mrs Victoria Swann
Tel: (0131 6)51 7607
Email: Vicky.Swann@ed.ac.uk 

