Computer Science

This course covers computational methods of knowledge, representation, and reasoning, tracing their origins in epistemology and the study of logic, and showing their evolution and use in artificial intelligence. Students discuss theories of knowledge and related developments in Artificial Intelligence in the context of the historic development of the field; and demonstrate knowledge of several established knowledge representation and reasoning methods such as sentential logic, semantic networks, ontologies, fuzzy systems, and Bayesian networks.

Computational processes are increasingly being discovered in natural, social, and economic systems as well as typical silicon-based computing devices such as laptops and smartphones. For those with little or no previous computing education, this course develops the necessary understanding and thinking skills so that such systems can be viewed as predictable, understandable, and ultimately controllable. It is valuable in its own right, as an underpinning now required in many other disciplines, and as a foundation for further study in computing science.

This course posits that over the last 40 years computing scientists have learned a lot about problem-solving. It introduces some of the techniques and strategies learned, such as a number of the fundamental concepts seen repeatedly throughout both studies and careers in computer science. The materials presented utilize puzzles and games, and do not require any computer skills.

This course introduces the essential elements of telecommunications in support of business activities. Topics include: OSI Model and TCP/IP Protocol Suite; LAN and WAN technology; voice and data communication technologies; communication architectures; networking and security; protocols and standards.

This course provides an overview of data management architectures and analytics procedures aimed at organizing, describing, and modeling big data, both structured and unstructured. The course discusses both technical aspects of data management/analytics and topics related to analysis managerial evaluation including how to translate the outputs into meaningful business insights. The course examines topics including relational databases such as OLTP, Data warehouse, and SQL language; big data and NoSQL databases, distributed file system, Hadoop, Spark, and Data Lake concept; data understanding and data preparation; models and statistical techniques applied to Big Data; regression and classification trees; ensemble methods (random forest and boosted trees); logistic regression; supervised artificial neural networks; models' performance evaluation; big data ingestion and management; data preparation and cleaning; machine learning algorithms application; and machine learning model evaluation. The course requires students have a basic understanding of descriptive and inferential statistics and basic computer skills as a prerequisite.

This course introduces the important field of machine learning. Students use a systematic approach, based on the following three key ingredients: tasks, models, and features. The course introduces both regression and classification, and studies emphasise concepts such as model performance and learnability. As part of this course students learn techniques such as linear regression, single and multiple layer perceptron classification, kernel-based models (including RBF and SVM), decision tree models and random forest, and Naïve Bayes classification and k-means clustering. Students are also introduced to techniques for pre-processing the data (including PCA).

This course explores neural networks and the mathematical equations that underlie them. Students build neural networks using state of the art simulation technology and apply these networks to the solution of problems. The course examines examples of computation applied to neurobiology and cognitive psychology.

This course introduces the design and modelling of software systems using object-oriented techniques. The course starts by exploring the use of modelling in software development. Students learn to document designs in the Unified Modeling Language, UML, with emphasis on class, sequence, and state diagrams and the Object Constraint Language, OCL. The course uses modern model-driven development tools and students discuss their strengths and weaknesses. The course looks at criteria that make one design better than another in context and introduce design principles and patterns that capture good practice.

The course synthesizes ideas from linguistics and computer science to provide students with a fast-paced introduction to the field of natural language processing. The course covers the most widely-used theoretical and computational models of language, including both statistical and non-statistical approaches. The course familiarizes students with a wide range of linguistic phenomena with the aim of appreciating the complexity, but also the systematic behavior of natural languages like English, the pervasiveness of ambiguity, and how this presents challenges in natural language processing. In addition, the course introduces the most important algorithms and data structures that are commonly used to solve many NLP problems. The course covers formal models for representing and analyzing the syntax and semantics of words, sentences, and discourse. Students learn how to analyze sentences algorithmically, using hand-crafted and automatically induced treebank grammars, and how to build interpretative semantic representations. The course also covers a number of standard models and algorithms that are used throughout language processing. Examples include n-gram and Hidden Markov Models, the EM algorithm, and dynamic programming algorithms such as chart parsing.