Computer Science

This introductory course in computer graphics comprises of three parts. The first part of the course presents a bird's-eye view of the current state-of-the-art in the field. The latter two parts cover rendering, which is one of the core topics in computer graphics, in detail. The second part of the course teaches central concepts in rendering, along with the relevant mathematics. Finally, the third part of the course focusses on applications of the theory taught in the second part.

The course provides a thorough introduction to graph and network analysis from a computer science perspective. It covers the basic concepts and key algorithms in network analysis, and discusses their use in the context of many real-world applications across a variety of domains. Students learn to apply network analysis methods in practice through the medium of the Python programming language. Students taking this course must have previously completed the module COMP30760 "Data Science in Python". or an equivalent class at their home university.

This course provides the theory and practice of knowledge graph construction, reasoning, and question answering technologies. The students analyze case studies to construct knowledge graphs and apply reasoning services on them. The course covers the following topics: knowledge graph foundation and standards; RDF (Resource Description Framework); OWL (Web Ontology Language); SPARQL (Query Language for RDF and OWL); knowledge graph construction, embeddings, and completion
knowledge graph reasoning and querying; tableaux algorithm; tractable schema reasoning in EL; tractable query answering in DL-Lite; and semantic parsing.

This interdisciplinary course provides students with the opportunity to address complex problems identified by industry, community, and government organizations, and gain valuable experience in working across disciplinary boundaries. In collaboration with a
major industry partner and an academic lead, students integrate their academic skills and knowledge by working in teams with students from a range of disciplinary backgrounds. This experience allows students to research, analyze and present solutions to a real-world problem, and to build on their interpersonal and transferable skills by engaging with and learning from industry experts and presenting their ideas and solutions to the industry partner.

This course walks the students through the complex set of concepts and projects that form the Big Data stack. Students learn how to set up Big Data environments, how to use efficient data management operations and how to run algorithms - to the scale and speed required by Big Data datasets. At the end of the course, students design and implement their own solutions to address Big Data problems.

This course provides an overview of the four research fields of computer science that bridge fundamental theories of computer science with the cutting-edge research in the Computer Science department at Tohoku University. The course consists of four parts, taught by four professors: algorithm theory, bioinformatics, communication network, and computability theory. 

The course provides a broad overview of the research areas in computer science. 

This course offers a study of the theory of automata and formal languages. Topics include: automata theory; finite automata; languages and formal grammars; regular languages; pushdown automata; Turing machine; compilers.

Artificial intelligence is a branch of computer science that studies computational models for various mental facilities of human intelligence and cognition. Recent AI deals with an extremely wide range of topics including machine learning, computer vision, natural language processing, to name a few. This course focuses on fundamental and traditional topics, including problem definition and solving, various search strategies, logic representation and inference, probabilistic models, reinforcement learning, game theory and mechanism design. 

In this course, student learn to process XML (with XSLT and Java), to model data with XML (XML native, RDF), and to query XML data (XQuery). The course teaches many concepts of data modelling and knowledge representation that are beyond the syntactic issues of XML or RDF. The knowledge students acquire in the course is fundamental to the many data design and data analytics tasks occurring in todays IT and business landscapes. The second part of the course is dedicates to advanced DB concepts including active databases, mobile databases, spatial and temporal databases, triggers, performance tuning, distributed databases, and indexing and query optimization. The third part of the course covers the modern, agile world of data processing: NoSQL. It is about the processing of semi-structured data, transforming data streams into formats (triplets, JSON) to be processed by new DB systems (e.g. MongoDB, CouchDB). 

The course provides basic knowledge in the field of artificial intelligence and machine learning for applications in medicine and health. The course covers the chain from medical databases via algorithms to regulations and requirements for diagnostic software.