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Computer Science—Artificial Intelligence (MSc, online, part-time)

This module introduces learners to the different categories of machine learning task and provides in-depth coverage of important algorithms for tackling them.

Statistical Relational Learning is an area of Artificial Intelligence and Machine Learning concerned with the representation of, and reasoning and learning with, uncertain (probabilistic) and relational domain knowledge (such as graphs, web links or symbolic facts). Planned

OverviewThe topic of Agents and Multi-Agent Systems, examines environment that involve autonomous decision making software actors to interact with their surroundings with the aim of achieving some individual or overall goal.

More recently, significant global attention has focussed on the vision of autonomous vehicles, which also follows the core principle of an agent attempting to achieve a set of defined goals.

The module will examine Adaptive Learning Agents through the use of Reinforcement Learning algorithms an area of Machine Learning, which focuses on training learners to choose actions which yield the maximum reward in the absence of prior knowledge.

The module takes a hands-on, practical approach to reinforcement learning theory, beginning with Markov Decision Processes, detailing practical learning examples in discrete environments and how to formulate a reinforcement learning task.

This module will build on the basic concepts with a view to delving deeper into core computer vision, machine learning and deep learning topics.

In addition we will examine a range of deep learning architectures ranging from AlexNet upto the current state of the art in this ever expanding field.

Deep learning based computer vision forms the core of many of the recent developments in this field and has been widely adopted as a core AI tool by all the key industrial players such as Google, Facebook, IBM, Apple, Baidu ...

This module is primarily aimed at those who aim to undertake research in computer vision or require a deeper understanding of the subject to address commercial computer vision development.

Computer vision applications span a wide range of disciplines including industrial/machine vision, video data processing, biomedical engineering, healthcare, astronomy, imaging science, sensor technology, multimedia and enhanced reality systems.

module is intended for students who have completed a first course in machine learning, and already have a good grounding in supervised learning topics including: classification and regression;

This module takes a practical approach to introducing learners to the strengths and weaknesses of human perception, and the use of best practices to represent complex and large data stories using visual primitives.

Large-scale data analytics is concerned with the processing and analysis of large quantities of data, typically from distributed sources (such as data streams on the internet).

Students learn about foundational concepts, software tools and advanced programming techniques for the scalable storage, processing and predictive analysis of high- volume and high-velocity data, and how to apply them to practical problems.

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Computer Science—Artificial Intelligence (MSc)

The course introduces some of the main theories and techniques in the domain of information retrieval.(Language of instruction: English)

major tasks including classification, regression, clustering, association learning, feature selection, and reinforcement learning;

algorithms for these tasks that may include decision tree learning, instance-based learning, probabilistic learning, support vector machines, linear and logistic regression, and Q-learning;

practical applications such as sensor data analysis, healthcare data analysis, and text mining to identify spam email;

In this module we will aim to understanding a broad range of applications and a unifying view of the field, and concentrate on two main types of methods: (1) metaheuristic optimisation and (2) exact methods for constrained optimisation.

We will spend time in-class on practical implementations, writing our own optimisation programs from scratch and also using state-of-the-art libraries.(Language of instruction: English)

The topic of Agents and Multi-Agent Systems, examines environment that involve autonomous decision making software actors to interact with their surroundings with the aim of achieving some individual or overall goal.

A typical agent environment could be a trading environment where an agent attempts to optimise energy usage, or the profitability of a transaction.

More recently, significant global attention has focussed on the vision of autonomous vehicles, which also follows the core principle of an agent attempting to achieve a set of defined goals.

The module will examine Adaptive Learning Agents through the use of Reinforcement Learning algorithms an area of Machine Learning, which focuses on training learners to choose actions which yield the maximum reward in the absence of prior knowledge.

The module takes a hands-on, practical approach to reinforcement learning theory, beginning with Markov Decision Processes, detailing practical learning examples in discrete environments and how to formulate a reinforcement learning task.

Students learn about the basic principles and building blocks of deep learning, and how to implement a deep neural network ‘from scratch’.

They also learning about software libraries and tools, and gain experience of applying deep learning in a range of practical applications.

module is intended for students who have completed a first course in machine learning, and already have a good grounding in supervised learning topics including: classification and regression;

Large-scale data analytics is concerned with the processing and analysis of large quantities of data, typically from distributed sources (such as data streams on the internet).

Students learn about foundational concepts, software tools and advanced programming techniques for the scalable storage, processing and predictive analysis of high- volume and high-velocity data, and how to apply them to practical problems.

Case studies used include: software project management, public health policy planning, and capacity planning.(Language of instruction: English)

This module covers the concepts and technology that are central to embedded image processing: fundamentals of imaging and sensor characteristics;

Topics include: graph theory, network modeling, social network analysis, community-finding techniques, models of information diffusion, link prediction, evaluation techniques.

The student will learn how to apply Web mining techniques to applications such as recommender systems, adaptive personalisation, authority ranking.

Statistical Relational Learning is an area of Artificial Intelligence and Machine Learning concerned with the representation of, and reasoning and learning with, uncertain (probabilistic) and relational domain knowledge (such as graphs, web links or symbolic facts). Planned

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