Concepts of Machine Learning
Using a combination of lectures and lab work, the module covers bio-inspired machine learning paradigms giving students knowledge of advanced features of various methods at the theoretical and practical levels. This is an MSc level module which is also offered to undergraduate students.
The module covers computational algorithms for learning from data, data-driven decision making and complex problem solving. It provides an introduction to machine learning methods, such as neural networks, fuzzy logic, fuzzy clustering, bio-inspired computing, and covers basic concepts of feature selection and generalisation.
- Discuss essential facts, concepts, principles and theories of machine learning.
- Discus fundamental issues relating to the design and implementation of systems that employ machine learning
- Apply theoretical understanding of machine learning paradigms to solve data modelling, classification and decision making problems.
- Recognise social, ethical & professional issues and risk involved in the application and use of machine learning methods.
- Recognise and analyse specifications appropriate to specific problems and plan strategies for their solution.
- Describe the process involved in the effective deployment of machine learning.
- Evaluate machine learning methods in terms of general quality attributes and possible trade-offs presented within the given problem.
Learning from data
Feature selection and generalisation
Supervised and unsupervised learning methods, and clustering as an unsupervised learning problem
Fuzzy logic and fuzzy clustering
Deep networks:architectures and learning algorithms
Natural computing: genetic algorithms, evolutionary algorithms, evolution strategies, swarm intelligence
Advanced learning and evolution schemes: ensembles, boosting, hybrid algorithms, neuro-evolution
No specific module is pre- or co- requisite but knowledge of calculus and linear algebra is essential as the module uses mathematical concepts, such as vector, matrices and their operations, functions and graphs, gradient, derivative. The module also uses trigonometry concepts, statistical concepts and the notion of probability, data structures, first-order and second-order optimisation methods and general algorithmic concepts. MATLAB and Python are used in the labs.
All dates and timetables are listed in programme handbooks, found in the downloads section of individual programme pages.
- Timetable of all departmental teaching events
- Term dates
- Timetable for the week ahead (including venue information)
Enrolled students can find their personal teaching timetable and location of classes on their My Birkbeck profile.
There is no coursework element for undergaduate students.
Two-hour written examination (100%)
- S. Marsland, Machine Learning: an Algorithmic Perspective, CRC Press.
- M. Negnevitsky, Artificial Intelligence: a Guide to Intelligent Systems, Addison Wesley.
- E. Alpaydin, Introduction to Machine Learning, MIT Press.
- R. Rojas, Neural Networks-A Systematic Introduction. Available online at: http://page.mi.fu-berlin.de/rojas/neural/
- S. Theodoridis, K. Koutroumbas, Pattern Recognition, Academic Press.
S. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press. Available online at: https://web.stanford.edu/~boyd/cvxbook/
R. Battiti, First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method, Neural Computation 4(2). Available online at: https://www.researchgate.net/publication/2498372_First-_and_Second-Order_Methods_for_Learning_Between_Steepest_Descent_and_Newton's_Method
L. Bottou, F.E. Curtis, J. Nocedal, Optimization Methods for Large-Scale Machine Learning. Available online at: https://arxiv.org/pdf/1606.04838.pdf
- Students will be pointed to papers available online and other Web resources on the subject.