Undergraduate Course: Introductory Applied Machine Learning (INFR10069)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 10 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||Since the early days of AI, researchers have been interested in making computers learn, rather than simply programming them to do tasks. This is the field of machine learning. 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; there is no output 'teacher signal'.
The primary aim of the course 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 20 credit course replaces INFR10063 Introductory Applied Machine Learning (10 credits).
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. Na´ve Bayes. Dimensionality Reduction. Clustering: K-means, Simple Gaussian Mixture Models, Hierarchical Clustering. Boosting Approaches. Model Averaging, Mixtures of Experts. Evaluation of Performance.
[We will also use a modern machine learning programming environment]
Entry Requirements (not applicable to Visiting Students)
|Prohibited Combinations|| Students MUST NOT also be taking
Introductory Applied Machine Learning (INFR10063) OR
Introductory Applied Machine Learning (INFR11182) OR
Introductory Applied Machine Learning (INFR11152)
||Other requirements|| This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser (lecturer).
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. Matrix 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 generalizations.
6 - Entropy: is useful, but will be covered in the lectures.
Programming requirements: Students should be able to program in a modern object-oriented language such as Python.
Information for Visiting Students
|Pre-requisites||Visiting students are required to have comparable background to that assumed by the course prerequisites listed in the Degree Regulations & Programmes of Study.
If in doubt, consult the course organiser (lecturer).
|High Demand Course?
Course Delivery Information
|Academic year 2018/19, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||Coursework - 50%
Exam - 50%.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
||Hours & Minutes
|Main Exam Diet S1 (December)||2:00|
|Resit Exam Diet (August)||2:00|
On completion of this course, the student will be able to:
- Explain the scope, goals and limits of machine learning, and the main sub-areas 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.
|Data Mining: Practical Machine Learning Tools and Techniques (Second Edition) I. H. Witten and E. Frank, Morgan Kaufmann, 2005. ISBN 0-12-088407-0|
|Course organiser||Dr Nigel Goddard
Tel: (0131 6)51 3091
|Course secretary||Miss Lisa Branney
Tel: (0131 6)51 7607