Postgraduate Qatar Computing Science 2026-2027

This course presents the fundamental techniques of Artificial Intelligence, used in system such as Google Maps, Siri, IBM Watson, as well as industrial automation systems, and which are core to emerging products such as self-driving vehicles. This course will equip the student to understand how such AI technologies operate, their implementation details, and how to use them effectively. This course therefore provides the building blocks necessary for understanding and using AI techniques and methodologies.

This course will deliver the most sophisticated Machine Learning methodologies and algorithms which would be illustrated across a wide range of applications including but not limited to images, videos, health, time series data, language processing, etc. This course provides students with the Machine Learning principles for continuing learning and working in the area of Data Science and Artificial Intelligence. 

One of the biggest challenges in Artificial Intelligence is evaluating how well AI systems work.   This course will provide students of our MSc in AI with knowledge of core evaluation concepts, approaches, tools, techniques and technologies; we will also look at software testing of AI systems.

This course will allow students to use cutting-edge AI technologies to investigate the creation and application of AI systems. Such tools include deep learning libraries and simulation environments.

This course aims to make students familiar with basic data mining and visualisation techniques and software tools. Students will learn how to analyse complex datasets by applying data pre-processing, exploration, clustering and classification, time series analysis, and many other techniques. This course will also cover text mining and qualitative modelling. Through this course students will be able to analyse real-world datasets in various domains and discover novel patterns from them. This course is particularly suitable for those who are interested in working as data analysts or data scientists in the future.

The aim of the course is to introduce students who have some background in computing to (1) the varied aims for which Natural Language Generation (NLG) is pursued, (2) the main rule based and statistical methods that are used in NLG, and (3) some of the main NLG algorithms and systems. The course will cover NLG both as a theoretical enterprise (e.g., for constructing models of language production) and as practical language engineering, paying particular attention to the link between NLG and data science. Some programming experience is expected.

Recent advances in AI have changed the perception of what machines can do, from on-line search to answering questions. An underlying feature of many AI systems concerns how knowledge is acquired, represented, and reasoned with. Companies such as Google, IBM, and Facebook have been developing sophisticated tools for knowledge representation and reasoning. This module provides the theory and practice of knowledge representation and reasoning, also presenting cutting-edge technologies, libraries and tools. At the end of the course students will be able to design, implement and evaluate knowledge-intensive AI systems.

This course will provide students of our MSc in AI programme with the opportunity to develop their own AI research project, under the supervision of a member of staff. Typical projects include extending, improving or adapting existing AI theories or techniques to solve different problems, comparing competing techniques or tools to solve a particular problem, and so on. Students will improve their problem-solving and communication skills, as well as broaden, deepen and consolidate knowledge obtained in other components of the degree.

This course will provide students of our MSc in AI programme with the opportunity to develop their own AI research project, under the supervision of a member of staff. Typical projects include extending, improving or adapting existing AI theories or techniques to solve different problems, comparing competing techniques or tools to solve a particular problem, and so on. Students will improve their problem-solving and communication skills, as well as broaden, deepen and consolidate knowledge obtained in other components of the degree.