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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Introductory Applied Machine Learning (Distance Learning) (INFR11139)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryOrganisations 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 distance-based course is aligned with the on-campus Agent Based Systems course (INFR10049), which uses a flipped classroom delivery approach. Distance students will cover the same materials, have the same coursework, engage in the same classroom activities (but using Collaborate) and take the same exam as the on-campus students.
Course description This course is delivered using "flipped-classroom" methods, and will be looking for student feedback during the course how this is working for you. 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 some of the lecture slots, we will have other activities to review the topic material.
1) Introductory Lecture

2) Maths and Probability

3) Thinking About Data

4) Naive Bayes

5) Decision Trees

6) Generalisation and Evaluation

7) Linear Regression

8) Logistic regression

9) Optimisation and Regularisation

10) Support Vector Machines

11) Nearest Neighbours

12) K-Means

13) Gaussian Mixture Models

14) Principal Components Analysis

15) Hierarchical Clustering

16) Neural Networks

We will also use a modern machine learning programming environment.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Introductory Applied Machine Learning (INFR10069)
Other requirements Practical Introduction to Data Science is recommended but not required.

Other Requirements:

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, m atrix 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 n - dimensional generalizatio ns.
6. Entropy: is useful, but will be covered in the lectures

Programming requirements:
Students should be familiar with programming in a modern object-oriented 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:
  1. Explain the scope, goals and limits of machine learning, and the main sub-areas of the field.
  2. Describe the various techniques covered in the syllabus and where they fit within the structure of the discipline.
  3. Students should be able to critically compare, contrast and evaluate the different ML techniques in terms of their applicability to different Machine Learning problems.
  4. 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. Given appropriate data students should be able to use a systematic approach to conducting experimental investigations and assessing scientific hypotheses.
Reading List
Books that may be useful, but are not required:

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
Additional Information
Graduate Attributes and Skills Problem solving, Analytical thinking, Handling complexity and ambiguity, Independent learning and development.
KeywordsDistance Learning,Informatics,Machine Learning
Course organiserDr Nigel Goddard
Tel: (0131 6)51 3091
Course secretaryMs Katey Lee
Tel: (0131 6)50 2701
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