Computer Science

This course provides a study of artificial intelligence and the application of AI tools and techniques to solve real-world problems. Topics include: knowledge representation techniques; types of searches (blind, heuristic); reasoning under uncertainty (Bayesian inference, Bayesian Networks, Markov Models, fuzzy logic); applied artificial intelligence.

The course examines the process of creating, assessing, and pursuing product opportunities, with emphasis on evaluating future markets shaped by new technologies. The focus is on technology innovation coupled with business planning and development. Other topics include factors that drive continuous creative product innovation, evaluation of new product ideas and risk assessment of commercialization, product development strategies in industrial marketing, understanding the behavior of the buyer and formulation and implementation of an innovative marketing strategy and business plan. This course combines mentor-guided team projects, case studies, and research on the entrepreneurial process. Text: Osterwalder and Pigneur, BUSINESS MODEL GENERATION: A HANDBOOK FOR VISIONARIES, GAME CHANGERS, AND CHALLENGERS. Assessment: exam I (25%), exam II (25%), assignments (10%), class activities (5%), class attendance and participation (5%), term project and presentation (30%).

This course covers the basic technical aspects of dynamic website construction concerning the front end (client) and the back end (server). It develops a springboard for web-related technologies that require data collection, organization and maintenance of accessible websites, through semantic layouts and with an introduction to object orientated coding practices. It focuses on developing skills in using HTML, CSS, jQuery, with PHP and SQL, and covers aspects of data flow and web data management.

The course offers an introduction to programming-based problem solving for social scientists. The course introduces a number of problems and solutions in social science data processing with applications in R. The course begins with a number of general programming topics, followed by efficient processing of different data structures and how data can be combined using SQL and Tidyverse. Secondly, the course looks at special challenges related to space and time. The spatial dimension introduces GIS techniques. Towards the end, the course sees how machine text analysis can be used to automate data collection and to look at how we can effectively visualize different types of data. The course provides a good basis for independent work with social science information. The course requires students to have completed a course in research methods and statistics as a prerequisite.

Operations Research is a field in which people use mathematical and engineering methods to study optimization problems in Business and Management, Economics, Computer Science, Civil Engineering, Electrical Engineering, etc. This course focuses on deterministic optimization techniques, a major part of the field of Operations Research. The course is divided into four modules: (1) Linear programming, (2) Integer programming, (3) Nonlinear programming, and (4) Dynamic programming.

This course introduces the fundamental principles, methodologies and practices of modern software engineering. It covers the software process, and development activities including requirements engineering, software design, testing, deployment and evolution. 

The course introduces theories and techniques of natural language processing and language technology. It covers the whole field, from speech recognition and synthesis to semantics and dialogue. The course focuses on industrial and laboratory applications, such as document retrieval on the Internet, information extraction, conversational agents, and verbal interaction in virtual worlds. Fundamental algorithms are described using Prolog or regular expressions. Topics covered in this course include an overview of language processing (applications, disciplines of linguistics, examples); Corpus and word processing (regular expressions, automata, an introduction to Perl, concordances, tokenization, counting words, collocations); morphology, transducers, and part-of-speech tagging; Prolog to write phrase-structure grammars (constituents, trees, using Prolog to do natural language analysis, DCG rules, variables, getting the syntactic structure, compositional analysis to get the semantic structure); syntactic formalisms (constituency and dependency, chart parsing, statistical parsing, functions, dependency parsing); semantics (formal semantics, lambda-calculus, compositionality such as nouns, verbs, determiners, words and meaning, lexical semantics, case grammars, semantic grammars); discourse and dialogue (rhetoric, anaphora, structure, RST, automata, pairs, speech acts, multimodality); and an overview of speech synthesis and speech recognition.

The goal of this course is to introduce the operation of computer systems at the level of Instruction Set Architectures (ISA). It provides a basic understanding of the design principles that govern modern computer architectures and their components. Special attention is paid to (super scalar) pipelining and memory hierarchy techniques including caches. Implementation and efficiency issues are exemplified. Metrical performance analysis methods are discussed to evaluate architectural alternatives. The course introduces the foundations of low-level computer functioning. The main topics include computer systems, low-level programming techniques, the techniques of RISC processors and pipelining, cache memory, and virtual memory. The course also illuminates the alternative design principles of modern computer architectures in order to provide an understanding of their impact on performance. Quantitative methods to evaluate design principles for performance constitute an important subject of the course.