Computer Science

This course covers the basic grammar of Java language and its programming ideas, use Java language to implement algorithms, and learn the common design, development, testing techniques and application development techniques of Java language (including Internet development and mobile application development). This course contains the learning of Java language grammar, program structure, design method and testing technology; object-oriented programming design ideas; multi-threaded concurrent processing technology, Internet connection access processing technology, program fault-tolerant processing mechanism; Android application development.

This course examines the close integration of music art and the latest information technology, which leads to the revolutionary change of modern music creation, performance, appreciation and communication mode and the main scientific knowledge and technical principles behind it.

This course introduces mathematical tools required in the study of computer science. Topics include: Logic and proof techniques: propositions, conditionals, quantifications; relations and functions: equivalence relations and partitions; partially ordered sets; Well-Ordering Principle; function equality; Boolean, identity, inverse functions; Bijection; mathematical formulation of data models (linear model, trees, graphs); counting and combinatoric: Pigeonhole Principle, Inclusion-Exclusion Principle; number of relations on a set, number of injections from one finite set to another, diagonalisation proof: An infinite countable set has an uncountable power set; Algorithmic proof: An infinite set has a countably infinite subset; subsets of countable sets are countable.

This course builds upon knowledge gained in introductory courses on functional programming, languages, and compilers. Using Haskell as the course's language of choice, students look at several advanced functional programming techniques, patterns, libraries, and tools. Course includes lectures, assignments, joint discussions, and programming exercise. Topics covered include: development of tools, testing, debugging and profiling; libraries of data structures, programming languages, monads, monad transformers, arrows, and applicative functors; language features and extensions of multi-parameter type classes and functional dependencies, type families, kinds, generalized algebraic data types (GADTs), existential types, and higher-rank polymorphism.

This course focuses on the organization and structure of a computer. Topics include: data representation; basic arithmetic; execution of instructions; assembly programming; main memory; cache memory; virtual memory; input/output systems.

The aim of the course is to give the necessary knowledge of digital image analysis for further research within the area and to be able to use digital image analysis within other research areas such as computer graphics, image coding, video coding, and industrial image processing problems. The course also prepares students for further studies in computer vision, multispectral image analysis, and statistical image analysis.