| CODE | ICS3221 | |||||||||
| TITLE | Classification Search and Optimisation | |||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | |||||||||
| MQF LEVEL | Not Applicable | |||||||||
| ECTS CREDITS | 6 | |||||||||
| DEPARTMENT | Intelligent Computer Systems | |||||||||
| DESCRIPTION | Machine learning is the study of systems that learn from data and are used in a wide variety of applications such as prediction, forecasting and pattern recognition. For instance, face detection, optical character recognition, and weather forecasting are all well-known machine learning problems. The aim of this study-unit is to introduce students to the area of Machine Learning with a special emphasis on Search and Optimisation problems: many important, “hard”, real-world problems are either poorly defined (e.g. modeling and forecasting) or are known to have exact solutions that require computationally intractable time to solve. Search and optimisation is concerned with finding approximate, yet highly accurate solutions to these problems very efficiently. Study-unit Aims: The aim of this study-unit is to introduce students to the following topics, algorithms and techniques: - An introduction to inductive and deductive learning systems; - Supervised and unsupervised learning models; - Clustering of data, classification problems and vector quantisation; - Inferring relationships between data for pattern recognition purposes; - Combinatorial and stochastic optimisation; - Monte Carlo methods, Genetic algorithms, Simulated Annealing and Ant Colony optimisation; - A brief introduction to Artificial Neural Networks and computational learning theory; - An introduction to “big data”. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Characterise different machine learning algorithms as supervised, unsupervised, inductive and deductive learning systems; - Demonstrate knowledge and understanding of common computational learning systems; - Describe the theoretical basis and properties of “hard” problems; - Identify the types of problems that can be solved using machine learning and approximate methods. 2. Skills: By the end of the study-unit the student will be able to: - Implement a complete solution to a “hard” computing problem; - Combine algorithms to create solutions to real-world problems; - Evaluate the suitability of a search and optimisation technique to a given problem; - Interpret and assess the results obtained when applying learning algorithms and approximate methods to real-world problems. Main Text/s and any supplementary readings: Study-unit notes and references given in class. |
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| RULES/CONDITIONS | Before TAKING THIS UNIT YOU ARE ADVISED TO TAKE CSA1017 | |||||||||
| STUDY-UNIT TYPE | Lecture and Independent Study | |||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Charlie Abela John M. Abela Alexiei Dingli Kristian Guillaumier |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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