• Introduction
• Basics of z transform theory
• inverse z transform
• convolution
• recursion relation
• realisability

• Sampling and reconstruction of signals
• zero order hold/D->A conversion
• Shannon's sampling theorem; aliasing and folding
• choice of the sampling period in sampled-data control systems
• pulse transfer function and analysis of control systems
• mapping of poles and zeroes

• Case study: PID digital control

• Continuous-time state-space systems and their discretization
• controllability and observability under discretization
• intersample behaviour

• Realization theory
• canonical forms
• minimality
• internal- and BIBO-stability, and relation between the two

• Controller design via pole placement
• continuous-time-based design techniques
• deadbeat control

• Case study: root-locus based digital control design

• Observers and their use in state-feedback loops
• Observer-based controllers
• the separation principle
• Optimal control design
• Finite horizon LQR
• Inifte Horzion LQR

Learning & teaching methods

Assessment methods

Referral Method: By examination

The course will cover the following topics:

• Review of mathematical background
• Review of state space modelling
• Review of linear algebra
• Review of probability
• Stochastic simulation and Monte Carlo method
• Random Number Generation
• Monte Carlo method
• Stochastic simulation using Monte Carlo simulation
• Stochastic signal processing, focusing on
• Estimation problem and least squares
• Kalman filtering and Extended Kalman filtering
• Particle Filtering
• Advanced system control theory
• Optimal Control: LQR and LQG
• Receding horizon methods
• A case study: next generation health care – electrical stimulation and robotic-assisted upper-limb stroke rehabilitation

Learning & teaching methods

Assessment methods

Referral Method: By set coursework assignment(s)

• Introduction to AI
  • Flavours of AI: strong and weak, neat and scruffy, symbolic and sub-symbolic, knowledge-based and data-driven.
  • The computational metaphor. What is computation? Church-Turing thesis. The Turing test. Searle's Chinese room argument.
• Search
• Finding satisfactory paths: depth-first and breadth-first, iterative deepening, local search and heuristic search. Finding optimal paths: branch and bound, dynamic programming, A*.
• Representing Knowledge
  • Production rules, monotonic and non-monotonic logics, semantic nets, frames and scripts, description logics.
• Reasoning and Control
  • Data-driven and goal-driven reasoning.
• Reasoning under Uncertainty
  • Probabilities, conditional independence, causality, Bayesian networks, belief propagation.
• Machine Learning
  • Inductive and deductive learning, unsupervised and supervised learning, reinforcement learning, concept learning from examples, Quinlan's ID3, classification and regression trees, Bayesian methods.

Learning & teaching methods

Assessment methods

referral is 3 hr exam.

Referral Method: By examination

• Historical Perspective
• Biological motivations: the McCulloch and Pitts neuron, Hebbian learning.
• Statistical motivations
• Theory
• Generalisation: What is learning?
• The power of machine learning methods: what is a learning algorithm? what can they do?
• Probability
• Probability as representation of uncertainty in models and data
• Bayes Theorem and its applications
• Law of large numbers and the Gaussian distribution
• Markov and graphical models
• Supervised Learning
• Classification using Bayesian principles
• Perceptron Learning
• Support Vector Machines and Kernel methods
• Neural networks/multi-layer perceptrons (MLP)
• Features and discriminant analysis
• Linear Algebra
• Using matrices to find solutions of linear equations
• Properties of matrices and vector spaces
• Eigenvalues, eigenvectors and singular value decomposition
• Data handling and unsupervised learning
• Principal Components Analysis (PCA)
• Blind source separation using Independent Components Analysis (ICA)
• K-Means clustering
• Spectral clustering
• Manifold learning
• Regression and Model-fitting Techniques
• Linear regression
• Polynomial Fitting
• Kernel Based Networks
• Optimisation
• Convexity
• 1-D minimisation
• Gradient methods in higher dimensions
• Constrained optimisation
• Dynamic Programming
• Case Studies
• Example applications: Speech, Vision, Natural Language, Bioinformatics.

Learning & teaching methods

Assessment methods

Referral Method: By examination

Learning & teaching methods

Assessment methods

Referral Method: By examination

This module addresses a large number of problems in web science, chosen by the students as individuals or in groups. Previous issues have included the following:

• What factors influence credibility on the Web?
• Does information want to be free
• What is misinformation and how and why does it spread on the Web?
• Does the Web have gatekeepers?
• Identity authenticity or identity anonymity?
• Should the web’s infrastructure directly protect any of the following: payments, privacy or piracy?
• The rights of individuals in the media spotlight to privacy in the digital environment appears confused and lacking direction. Is there a balance to be struck here and if so on what principles should it be based?
• What effect is the Web having on University operation?
• How can we create/utilise and spread Web memes e.g. viral videos for government to improve public health/public understanding of an issue?
• Should there be an international law of the web as there is an international law of the sea?
• Who is shaping the education web - students or faculty?
• What are the barriers to total adoption of the semantic web across different industries?
• Will true media convergence require a more open, and less 'walled' publishing web?
• Assess the problems associated with the development of a coherent policy for the regulation of Internet content in the EU.
• Can the Web reduce poverty? 

Learning & teaching methods

Assessment methods

Referral Method: By set coursework assignment(s)

• Rationale behind Database Systems
• Database Modelling using the Entity-Relationship Model
• Database System Architecture
• Data Models and Data Sublanguages
• SQL
• Database Management Issues: concurrency, security, integrity
• Application of Database
• No SQL databases

Learning & teaching methods

Assessment methods

The initial coursework will be on line from week one. However a hand-in of the database design is required in week 3. Formative feedback and in class discussion of an appropriate design will be given in week 4. This will then be used to answer the remaining part of the assignment. None engagement in the excise will limit your understanding of the problem to be addressed.

Referral Method: By examination

• Introduction
• Data Objects, Data Structures, Complex Data Structures
• Algorithm Analysis
• The big O
• Correctness
• Algorithm Design and Strategies
• Brute Force, Depth-First, Breadth-First, DFID, Best-first, Greedy, Divide and Conquer, Dynamic Programming, Branch and Bound
• Simple Data Structures
• List, Stack, Queue, Tree, Tree traversal
• Sorting
• Selection Sort, Insertion Sort, Shellsort, MergeSort, QuickSort, Bucket Sort, Radix sort, External sorting
• Searching
• Sequential Search, Handling Failure, Binary Search, Binary Tree Search,
• Advanced Tree Structures
• AVL Trees, Retaining Balance, Single Rotation, Double Rotation
• Splay Trees, Red-black Trees, B-trees
• Hash tables
• Terminology, Hash table size, Hash function collision resolution, Separate chaining, Open Addressing, Re-hashing
• Priority Queues (Heaps)
• Terminology, Simple implementations, Binary heaps, Heap sort
• Graphs
• Terminology, Adjacency Matrix and List, Connectivity, Breadth vs Depth first search, Topological sort, Shortest path algorithms, Unweighted graphs, Breadth first search, Weighted Graphs, Minimum Spanning Tree, Prim's algorithm, Biconnectivity, Articulation points
• Geometric algorithms
• Convex hull, …

Learning & teaching methods

Assessment methods

Referral Method: By examination

• Graphical User Interface Programming
• Writing Swing and AWT user interfaces
• User interfaces Components
• Event Handling
• Graphics in User Interfaces
• Control Flow and the Java Virtual Machine
• JVM overview
• Exceptions and exception handling
• Recursion in the JVM
• Multi-threading and synchronisation
• Storage and Files in Java
• Garbage Collection
• Strings and Character Encodings
• Input/Output and Object Serialisation
• Validation and Verification
• Black Box Testing
• White Box Testing
• Integration Testing Strategies
• The C programming language
• Introduction to the language
• Pointers and pointer arithmetic
• Data structures and arrays
• Comparison with Java
• Patterns

Learning & teaching methods

Assessment methods

Referral Method: By set coursework assignment(s)

• Goal-structured requirements analysis;
• Design goals;
• System/service decomposition;
• Component models;
• Formal methods in industry;
• Modelling in Event-B;
• Proof in Event-B;
• Model checking in Event-B;
• Model structuring and refinement;
• Model decomposition;
• Reasoning about programs;
• Preconditions, postconditions, loop invariants, loop variants

Learning & teaching methods

Assessment methods

Referral Method: By examination