Postgraduate Course: Applied Machine Learning (INFR11211)
Course Outline
School  School of Informatics 
College  College of Science and Engineering 
Credit level (Normal year taken)  SCQF Level 11 (Postgraduate) 
Availability  Available to all students 
SCQF Credits  20 
ECTS Credits  10 
Summary  ***This course replaces Introductory Applied Machine Learning (INFR11182)***
Machine learning is the study of computer algorithms that learn and adapt from data, as opposed to being programmed to explicitly follow instructions. This course will discuss two main branches of machine learning: (1) Supervised Learning, which is concerned with learning to predict an output, given inputs, and (2) Unsupervised Learning, by which we wish to discover the structure embodied in data, without having access to additional information about the data, e.g. labels.
This course will equip the students with knowledge and a set of practical tools that can be applied to solve realworld machine learning problems. This achievement of this aim is underpinned by a principled approach to understanding the problem space and formulating a solution. 
Course description 
Delivery:
The course will be delivered through a combination of: (1) prerecorded videos lectures, (2) live question/answer and example sessions, (3) practical labs, (4) tutorials, and (5) an online discussion forum.
Content:
The exact set of methods and algorithms explored in the course will vary slightly from year to year, but will include many of the following topics:
 Introduction to machine learning
The learning problem, supervised vs unsupervised learning
 Representing data
Categorical vs real valued attributes, feature extraction, basis expansion
 Classification
Naive Bayes, logistic regression, nearest neighbours, decision trees, neural networks
 Regression
Linear regression
 Ethics of machine learning
Fairness, biases in data, responsible application of machine learning methods
 Fitting models to data
Optimization, generalization
 Unsupervised learning
Dimensionality reduction, PCA, clustering
 Evaluating machine learning models
Accuracy, precision and recall, ROC curves

Entry Requirements (not applicable to Visiting Students)
Prerequisites 

Corequisites  
Prohibited Combinations  Students MUST NOT also be taking
Machine Learning and Pattern Recognition (INFR11130) OR
Introductory Applied Machine Learning (INFD11005) OR
Machine Learning (INFR10086) OR
Introductory Applied Machine Learning (INFR11182) OR
Introductory Applied Machine Learning (INFR10069) OR
Data Analysis and Machine Learning 4 (ELEE10031)

Other requirements  Maths requirements:
Linear algebra: Vectors: scalar (dot) product, transpose, unit vectors, vector length and orthogonality. Matrices: addition, matrix multiplication, matrix inversion, eigenvectors and determinants.
Special functions: properties and combination rules for logarithm and exponential.
Calculus: Rules for differentiation of standard functions.
Geometry: Basics of lines, planes and hyperplanes. Coordinate geometry of circle, sphere, ellipse, and ndimensional generalizations.
Probability theory: Discrete and continuous univariate random variables. Expectation and variance. Univariate and multivariate Gaussian distributions. Joint and conditional distributions.
Programming requirements:
Students should be familiar with programming in a modern objectoriented language, ideally Python which is the course language. 
Information for Visiting Students
Prerequisites  This course is only available to visiting students taking the majority of their credits in the school of Informatics who are either on a general or subjectspecific exchange.
Maths requirements:
Linear algebra: Vectors: scalar (dot) product, transpose, unit vectors, vector length and orthogonality. Matrices: addition, matrix multiplication, matrix inversion, eigenvectors and determinants.
Special functions: properties and combination rules for logarithm and exponential.
Calculus: Rules for differentiation of standard functions.
Geometry: Basics of lines, planes and hyperplanes. Coordinate geometry of circle, sphere, ellipse, and ndimensional generalizations.
Probability theory: Discrete and continuous univariate random variables. Expectation and variance. Univariate and multivariate Gaussian distributions. Joint and conditional distributions.
Programming requirements:
Students should be familiar with programming in a modern objectoriented language, ideally Python which is the course language.

High Demand Course? 
Yes 
Course Delivery Information

Academic year 2023/24, Available to all students (SV1)

Quota: None 
Course Start 
Semester 1 
Timetable 
Timetable 
Learning and Teaching activities (Further Info) 
Total Hours:
200
(
Lecture Hours 20,
Seminar/Tutorial Hours 4,
Supervised Practical/Workshop/Studio Hours 4,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
166 )

Assessment (Further Info) 
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %

Additional Information (Assessment) 
Written exam 60%
Coursework 40% 
Feedback 
Some of the live class sessions will be devoted to discussing practical examples (including some examlike questions), and providing feedback on student answers at the course level. Feedback at the course level will also be provided for the assessed and nonassessed courseworks. Piazza will be utilized for peerfeedback. 
Exam Information 
Exam Diet 
Paper Name 
Hours & Minutes 

Main Exam Diet S1 (December)  Applied Machine Learning  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 and critically compare the various techniques covered in the syllabus, and explain where they fit within the structure of the discipline
 apply the taught techniques to data sets to solve machine learning problems, using appropriate software
 analyse machine learning techniques in terms of their limitations and applicability to different machine learning problems and potential ethical concerns
 compare and evaluate the performance of applicable machine learning techniques in a systematic way

Reading List
Books that may be useful, but are not required:
"Probabilistic Machine Learning: An Introduction", Kevin Patrick Murphy "Pattern Recognition and Machine Learning", Christopher Bishop 
Additional Information
Graduate Attributes and Skills 
The student will be able to do the following:
 Apply critical and analytical thinking to realworld data problems
 Develop their problemsolving skills so they can better create, identify, and evaluate options in order to solve complex problems
 Develop the technical skills required to manipulate data and apply computational tools in order to make predictions from data
 Recognise and understand the ethical questions related to the application of machine learning algorithms 
Keywords  Machine Learning,Supervised Learning,Unsupervised Learning,Data Science,AML 
Contacts
Course organiser  Dr Oisin Mac Aodha
Tel: (0131 6)51 3292
Email: oisin.macaodha@ed.ac.uk 
Course secretary  Ms Lindsay Seal
Tel: (0131 6)50 2701
Email: lindsay.seal@ed.ac.uk 

