Undergraduate Course: Introductory Applied Machine Learning (INFR09029)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 9 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||***PLEASE NOTE: This course has been replaced with INFR10063 Introductory Applied Machine Learning***
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.
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 suite of machine learning software, e.g. WEKA.]
Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Human-Computer Interaction (HCI), Intelligent Information Systems Technologies, Natural Language Computing, Simulation and Modelling, Theoretical Computing
Entry Requirements (not applicable to Visiting Students)
||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.
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: None.
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 lecturer.
|High Demand Course?
Course Delivery Information
|Not being delivered|
| 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.
|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||Ms Beth Muir
Tel: (0131 6)51 1513