Postgraduate Course: Introductory Applied Machine Learning (Semester 2) (INFR11205)
Course Outline
School  School of Informatics 
College  College of Science and Engineering 
Credit level (Normal year taken)  SCQF Level 11 (Postgraduate) 
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.
*Delivery Note: This Semester 2 course is only available to students on the MSc in Finance Technology and Policy (within the Business School) and the MSc in Advanced Technology for Financial Computing. All other students should enrol on INFD11005 (for DSTI only), INFR11182 (for all other MSc) or INFR10069 (for UG). 
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 (INFD11005) OR
Machine Learning and Pattern Recognition (INFR11130) OR
Introductory Applied Machine Learning (INFR11182)

Other requirements  *Delivery Note: This Semester 2 course is only available to students on the MSc in Finance Technology and Policy (within the Business School) and the MSc in Advanced Technology for Financial Computing. All other students should enrol on INFD11005 (for DSTI only), INFR11182 (for all other MSc) or INFR10069 (for UG).
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

Academic year 2020/21, Not available to visiting students (SS1)

Quota: None 
Course Start 
Semester 2 
Timetable 
Timetable 
Learning and Teaching activities (Further Info) 
Total Hours:
200
(
Lecture Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )

Assessment (Further Info) 
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %

Additional Information (Assessment) 
Written Exam: 50%
Coursework: 50% 
Feedback 
There is a rich and wellresourced level of engagement between students and worldleading Informatics teaching and research staff:
 Extensive use of the School and University level virtual learning environments (VLE) such as Learn and social platform.
 Course forums will allow students to ask questions to both teaching staff and to other students.
 Peerfeedback as well as tutorfeedback is designed into all the tutorials, labs and coursework (formative and summative).
The Piazza platform is the place to ask questions about the course materials: topics slides and videos, the labs, tutorials and the assignments. Sign up for it at the link in the announcement. We encourage students to answer questions if you can  it is a great learning experience to explain something to another student. The forum is monitored and responded to by the lecturer and the TA. If you have issues that should be kept confidential, then of course please do email the course lecturer, but otherwise use the forum  it is more efficient and it benefits everyone. 
Exam Information 
Exam Diet 
Paper Name 
Hours & Minutes 

Main Exam Diet S2 (April/May)   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.
 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, use appropriate software to apply these techniques to the data set to solve the problem.
 Given appropriate data, use a systematic approach to conducting experimental investigations and assessing scientific hypotheses.

Reading List
Books that may be useful, but are not required:
 HandsOn Machine Learning with ScikitLearn & Tensor Flow. Aurelien
Geron, O'Reilly, 2017.
 Elements of Statistical Learning by T. Hastie, R. Tibshirani and
J. Friedman (Springer 2009)
 Bayesian Reasoning and Machine Learning by D. Barber (CUP, 2012)
 Data Mining: Practical Machine Learning Tools and Techniques, Ian
H. Witten, Eibe Frank, Mark A. Hall, 3rd edition, Morgan Kaufmann, 2011. 
Additional Information
Graduate Attributes and Skills 
Problem solving, Analytical thinking, Handling complexity and ambiguity, Independent learning and development. 
Keywords  Machine Learning,Data Science,IAML,Online Learning,Informatics 
Contacts
Course organiser  Dr Tiejun Ma
Tel: (0131 6)51 5642
Email: tiejun.ma@ed.ac.uk 
Course secretary  Ms Lindsay Seal
Tel: (0131 6)50 2701
Email: lindsay.seal@ed.ac.uk 

