An introduction to the various layers that make up a modern computer system: encoding of data and instructions, hardware, low-level programming, operating systems, applications and communications. A practical introduction to computing. Topics include: web design, an overview of computer hardware and operating systems, effective use of common applications, using the internet as a communication medium, applying programming concepts, and social implications of technology.
Basic mathematical tools and methods needed for computer science are introduced. Elementary mathematical skills for defining, analysing and reasoning with abstracts objects used in programming are developed. Topics include integers and rational numbers, strings and languages, methods of proof including induction , propositional logic, and elementary introductions to graphs, trees, counting and probability.
Fundamental programming techniques and processes, such as conditionals, iteration, recursion, functions, testing and debugging. Efficient ways to organise and manipulate data, including sorting and searching algorithms. Writing software that uses and implements common abstract data types such as lists, stacks, queues, dictionaries and trees. The low level representation of data and algorithms in the computer. An introduction to computer organisation.
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The instruction execution model. Assembly and disassembly of instructions.
Assembly language programming. How a high-level language is implemented at the machine level.
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Hardware support necessary to implement a secure multi-user operating system. An introduction to data communications: the OSI reference model, particularly how the lower layers combine to implement the application layer.
An introduction to secure communication and computer systems. An introduction to the analysis of algorithms and data structures. Common abstract data types and their implementations. Asymptotic complexity analysis.
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Sorting and searching algorithms. Depth-first and breadth-first search and applications. Graph optimisation problems. An introduction to logic, principles of counting, mathematical induction, recursion, relations and functions, graphs and trees, and algorithms. This course is suited to students who are interested in the foundations of computer science, mathematics and logic. An introduction to object-oriented, concurrent and functional programming. An introduction to software development, including processes, best practices, tools and quality assurance techniques such as testing.
An introduction to research topics in computer science. Students will be expected to prepare and deliver a review of research in a topic of their choice. Research articles will be provided during the course, and will consist of key scientific publications. Modern processor architectures. The structure of data communications and networks, including the internet, covering all levels of the communications architecture.
The layered protocol model, data transmission and coding, link-level and local area network protocols, wide-area internet working, routing, transport and security protocols. Basic application protocols as the foundation for distributed computing. Introduces various concepts related to software, system and network security.
Covers a range of topics including attacks on privacy and attack surface, static and dynamic analysis of malware, hardware security trusted computing base, secure boot, and attestation , network security and some hot topics in cryptography including elliptic curve, blockchain and bitcoin. Fundamental design techniques used for efficient algorithmic problem-solving and software development. Methods that yield algorithms that are both provably correct and efficient.
Efficiency of algorithms to provide a basis for deciding which algorithm is best for the job. Limits on the power of computers and the theory of NP-completeness. An introduction to methods whose correctness or performance is not guaranteed. Students will understand how to develop large-scale software systems, and learn about the issues associated with large-scale software systems and techniques for addressing them. Covers functional programming concepts, with applications to data integration from heterogeneous and asynchronous collections.
Building web and cloud clients and services, with emphasis on high-level declarative and functional techniques. Dynamic web applications. Security and performance as overarching factors of web application development. Operating system principles. Multi-user systems. Concurrent processes, threads, synchronisation and deadlock. Memory allocation and virtual memory. Managing files, disks and other peripherals. Security, protection and archiving. Distributed systems and algorithms, location, migration and replication transparency.
Real-time requirements. History of operating systems. Human behaviour and humans' expectations of computers.
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Computer interfaces and the interaction between humans and computers. The significance of the user interface, interface design and user centred design process in software development.
Interface usability evaluation methodologies and practice. Includes a group development and evaluation project using current implementation techniques and tools. The aim of this course is to present mathematical models for programming languages and computation, and derive some theorems regarding what can and cannot be computed. Abstract programming languages finite automata, context-free grammars, Turing and register machines are studied.
Basic concepts for programming languages, limits on computational power and algorithmic complexity are presented. Church-Turing thesis and quantum computing are briefly and critically discussed. Database principles. Machine learning is a branch of artificial intelligence concerned with making accurate, interpretable, computationally efficient, and robust inferences from data to solve a given problem. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
The cornerstones of AI: representation, utilisation, and acquisition of knowledge. Taking a real world problem and representing it in a computer so that the computer can do inference. Utilising this knowledge and acquiring new knowledge is done by search which is the main technique behind planning and machine learning. Computational biology is the development and application of computer algorithms and software to address scientific questions in the biological and life sciences, often using big data.
This course includes probabilistic computer modelling, computer-based statistical inference and computer simulation for, and motivated from, the life sciences. It focuses on modelling and analysing real-world biological data with an emphasis on analysing DNA sequence data.
Basic geometric processes including transformations; viewing and projection; back projection and ray tracing. Graphics modelling concepts: primitives, surfaces, and scene graphs, lighting and shading, texture mapping, and curve and surface design. Graphics and image processing fundamentals: image definition and representation, perception and colour models, grey level and colour enhancement, neighbourhood operations and filtering. Use of the OpenGL graphics pipeline. Each student taking one of these courses will be expected to do an individual practical project under the supervision of a member of staff.
Only students with excellent academic records will be allowed to take these courses, and only after a supervisor and topic have been agreed upon by the Head of Department. Students will be expected to apply the research methods in a collaborative research project. Prerequisite: Minimum GPA of 5.
edukacinesprogramos.lt/profiles/untraceable/iphone-spia-come-si-installa.php Students work in small groups to complete a substantial problem applying the knowledge learnt from the different courses in the Computer Science major. Teams are expected to reason on a problem, devise a solution, produce an artefact and present their work. The capstone provides an opportunity for students to further develop their technical and communication skills. Bento Bitsavers. Trakhtenbrot Algorithms and Complexity - Herbert S.