Postgraduate Course: Introductory Applied Machine Learning (INFR11152)
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Summary  *This course has been replaced by 'Introductory Applied Machine Learning INFD11005' from 2019/20*
*This course is for distance learning students within the School of Informatics and students on the Data Science, Technology & Innovation programme*.
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.
This distancebased course is aligned with the oncampus Introductory Applied Machine Learning course (INFR11181), which uses a flipped classroom delivery approach. Distance students will cover the same or similar materials, have the same coursework, engage in the same or similar classroom activities, and take the same exam as the oncampus students. 
Course description 
This course is delivered using "flippedclassroom" methods. Intellectual content will be delivered via a combination of online short video segments (overall, per topic, approximately the same length as a traditional lecture). Some of the topics have online quizzes associated with them, intended for you to review your understanding. During most of the lecture slots we will have other activities to review the topic material, also available by online recording.
We expect to cover the following general areas:
1. Review of maths and probability
2. Feature engineering (e.g., basis transforms, selection , Principal Components Analysis)
3. Classification vs. Regression
4. Supervised methods (e.g., Naive Bayes, Decision Trees and Random Forests, Linear & Logistic Regression, Support Vector Machines, Nearest Neighbours, Neural Networks)
5. Unsupervised clustering methods (e.g., kMeans, Gaussian Mixture Models, Hierarchical Clustering)
We will use a modern machine learning programming environment and industrystandard libraries.

Entry Requirements (not applicable to Visiting Students)
Prerequisites 

Corequisites  
Prohibited Combinations  Students MUST NOT also be taking
Introductory Applied Machine Learning (INFR10069) OR
Introductory Applied Machine Learning (INFR11182) OR
Introductory Applied Machine Learning (INFR10063)

Other requirements  Students should check these maths and programming requirements carefully, as the course assumes and builds on these foundations. Experience has shown that students without this background can struggle with the course.
Maths requirements:
1. Linear algebra: Vectors: scalar (dot) product, transpose, unit vectors, vector length, orthogonality. Matrices: addition, matrix multiplication, matrix inversion. Eigenvectors, determinants quadratic forms.
2. Special functions: properties and combination rules for logarithm and exponential.
3. Calculus: Rules for differentiation of standard functions. Functions of several variables. Partial differentiation. Multivariate maxima and minima.
4. Geometry: Basics of lines, planes and hyperplanes. Coordinate geometry of circle, sphere, ellipse, ellipsoid and ndimensional generalizations.
5. Probability theory: Discrete and continuous univariate random variables. Expectation, variance. Univariate Gaussian distribution. Joint and conditional distributions.
Programming requirements:
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.
 5. Planning, executing and evaluating significant experimental investigations, informed by developments at the forefront of the field

Reading List
Books that may be useful, but are not required:
 Pattern Recognition and Machine Learning by C. Bishop (Springer, 2006)
 Elements of Statistical Learning by T. Hastie, R. Tibshirani and
J. Friedman (Springer 2009)
 Bayesian Reasoning and Machine Learning by D. Barber (CUP, 2012)
 Machine Learning by T. Mitchell (McGraw Hill, 1997) 
Additional Information
Graduate Attributes and Skills 
Problem solving, Analytical thinking, Handling complexity and ambiguity, Independent learning and development. 
Keywords  Informatics,Machine Learning,Online Learning,Machine Learning,Data Science 
Contacts
Course organiser  Dr Nigel Goddard
Tel: (0131 6)51 3091
Email: Nigel.Goddard@ed.ac.uk 
Course secretary  Mrs Sam Stewart
Tel: (0131 6)51 3266
Email: Sam.Stewart@ed.ac.uk 

