Artificial Intelligence with a Year in Industry MSc

Royal Holloway, University of London

This module will describe the key principles of academic integrity, focusing on university assignments. Plagiarism, collusion and commissioning will be described as activities that undermine academic integrity, and the possible consequences of engaging in such activities will be described. Activities, with feedback, will provide you with opportunities to reflect and develop your understanding of academic integrity principles.

This module focuses on acquiring a deep understanding of foundational AI principles and techniques to model complex real-world problems as well as writing algorithms and problems to solve them. The module will start with an introduction to AI that will define core AI concepts, provide the philosophical foundations of AI and discuss ethical issues in this field. The module will continue by covering intelligent agents and classical search to then move to local search and optimisation algorithms. Finally, adversarial search and constraint satisfaction problems will also be taught. All these topics will be covered both from a theoretical point of view, during the lectures, and from a practical point of view during the labs.

This specialist module focuses on acquiring a deep understanding of the principles and techniques that are needed to design and build autonomous intelligent systems (AISs). The module will start with an introduction to AISs and real-world examples of them. It will then cover knowledge representation and engineering techniques based on formal logic. The module will then tackle autonomous decision making techniques, from AI planning to probabilistic reasoning and Markov Decision Processes. Reinforcement learning and techniques for cooperation and coordination between artificial agents will also be taught. All these topics will be discussed both from a theoretical point of view, during the lectures, and from a practical point of view, during the labs.

This module covers algorithm-independent machine learning; unsupervised learning and clustering; exploratory data analysis; Bayesian methods; Bayes networks and causality; and applications, such as information retrieval and natural language processing. You will develop skills in data analysis, including data mining and statistics.

This course is designed to enhance your awareness of the many ethical implications of working with advanced technology. The course recognises that the ethical issues in computing and AI come to the forefront through developments in technology, bringing new responsibility for novel ethical, social, and legal implications of technology almost on a daily basis.

The aim of this module is to explain the fundamental principles and quantitative methods in the design and analysis of computational experiments, notions that are at the core of current research and practice in AI. The theoretical concepts taught will be complemented by code examples, through which the student can gain hands-on experience in the methods taught.

The aim of this module is to teach the necessary background knowledge and practical techniques - especially deep learning - needed to apply natural language processing to large, real-life text-based projects. A brief survey of computational linguistic theory will include notions of syntax, semantics, and pragmatics. Practical techniques for preparing and pre-processing text will be taught in lab sessions. Typical commercial applications of NLP will be surveyed, with practical examples. Standard NLP techniques covered will include: topic modelling and LDA, and construction of word-embeddings.

In this module you will learn how to use MATLAB (Matrix Laboratory) and WEKA (Waikato Environment for Knowledge Analysis) as tools for machine learning and data mining. For MATLAB, you will develop an understanding of how to input and output data using vectors, arrays and matrics; learn techniques in data visualization, including plots in 2 and 3 dimensions, scatter plots, barplots, and histograms; and learn how to implement concepts from linear algebra and statistics, including probability and matrix decompositions. For WEKA, you will develop an understanding of how to use the software as a tool for training and testing, predicting generalisation performance, and cross-validation; and learn how to implement decision trees, na? Bayes classifiers, and clustering methods.