Computer Science (Artificial Intelligence) BSc

Program Overview

Students gain a solid grounding in computer science fundamentals, develop practical software engineering skills, and conduct independent research in specialized areas of AI through a dissertation project. The program emphasizes a research-led education, industry experience, and ethical considerations, preparing graduates for diverse and fulfilling careers in the rapidly evolving field of AI.

Program Outline

Computer Science (Artificial Intelligence) BSc - University of Sheffield

Degree Overview:

This program focuses on the study of machine intelligence, including bio-inspired algorithms, their relationship to living intelligence, and the nature of consciousness itself. It aims to position students at the forefront of exploring artificial intelligence and its implications. The program provides a solid grounding in computer science fundamentals during the first year. The second year introduces students to the practical aspects of software engineering through a real-world project for a real client.

• Gain a strong foundation in computer science fundamentals.
• Develop practical software engineering skills through real-world projects.
• Conduct independent research and explore specialized areas of AI through a dissertation project.
• Graduate with technical skills, teamwork abilities, effective communication skills, and system design expertise.

Outline:

Year 1:

• Core Modules:
• Introduction to Software Engineering:
Introduces software engineering concepts for developing functional software systems. Includes team-based project development for practical experience. (20 credits)
• Foundations of Computer Science:
Develops mathematical concepts and techniques foundational to computing. (20 credits)
• Java Programming:
Introduces programming concepts through the Java language, emphasizing program design and testing. (20 credits)
• Machines and Intelligence:
Introduces Artificial Intelligence, key concepts, and problems. Includes practical programming examples and robot programming experience. (20 credits)
• Devices and Networks:
Covers computer architecture elements, including digital logic, computer arithmetic, and instruction set architecture. Also explores network principles and protocols. (20 credits) Covers responsive design, accessibility, and legal issues. (10 credits)
• Introduction to Algorithms and Data Structures:
Introduces the design and analysis of efficient algorithms and data structures. Covers searching, sorting, graph algorithms, and combinatorial problems. (10 credits)
• Global Engineering Challenge Week:
A compulsory week-long project involving multi-disciplinary teams working on real-world challenges in areas like water, waste management, energy, and digital. (Variable credits)

Year 2:

• Core Modules:
• Data Driven Computing:
Introduces machine learning and pattern processing with a focus on applications. Employs a practical Python-based approach. (20 credits)
• Systems Design and Security:
Covers software systems design, highlighting security issues. Includes topics like requirements capture, database design, software architectures, and security threats. (20 credits)
• Robotics:
Explores the design and implementation of robotics technology, spanning psychology, computer science, and robotics. (20 credits)
• AI Group Project:
Provides experience in engineering a real software system for a real client in a competitive environment. Covers project management tools and technologies for delivering AI products. (20 credits)
• Logic in Computer Science:
Introduces the foundations of logic in computer science, covering propositional and predicate logics, natural deduction, and applications in automated reasoning and decision procedures. (10 credits)
• Functional Programming:
Introduces functional programming principles using the Haskell language. Covers types, classes, function definitions, and data types. (10 credits)
• Automata, Computation and Complexity:
Introduces theoretical foundations for computing systems, including finite state machines, pushdown automata, and Turing machines. Explores computability and computational complexity. (20 credits)
• Engineering - You're Hired:
A compulsory week-long project involving multi-disciplinary teams working on real-world problems provided by industrial partners. (Variable credits)

Year 3:

• Core Modules:
• Dissertation Project:
A major individual research project involving software design or experimental investigation. Includes a written dissertation and presentation. (40 credits)
• Accounting and Law for Engineers:
Introduces key areas of accounting and legal risk relevant to engineering practice. Covers budgeting, financial risks, contracts, environmental regulation, and intellectual property. (10 credits)
• Optional Modules:
• Modelling and Simulation of Natural Systems:
Introduces techniques for modelling and simulating dynamic natural systems using differential equations and individual-based methods. (10 credits) (10 credits)
• Theory of Distributed Systems:
Explores theoretical foundations for analyzing and designing concurrent, distributed, and mobile systems using process calculi. (10 credits)
• Reinforcement Learning:
Teaches the theory and implementation of reinforcement learning, including supervised learning, temporal difference learning, and deep reinforcement learning. (10 credits)
• Computer Security and Forensics:
Introduces computer security and forensics, focusing on building secure systems and responding to security breaches. (10 credits)
• Speech Processing:
Investigates the representation of speech in articulatory, acoustic, and auditory domains, and explores computational approaches to speech parameter extraction. (10 credits)
• 3D Computer Graphics:
Introduces techniques used in modern 3D computer graphics, covering 3D representations, light reflection models, realism techniques, and 3D animation. (10 credits)
• The Intelligent Web:
Explores opportunities and challenges of the intelligent web, covering technologies like advanced web tools, offline and multimodal interaction, and web 3.0. (10 credits)
• The Internet of Things:
Covers the context, hardware, communication protocols, security systems, and cloud-side analytics of the Internet of Things. Includes practical hands-on experience with IoT devices. (10 credits)
• Software Reengineering:
Introduces techniques for reverse-engineering, appraising, and reengineering legacy software systems to improve their quality and viability. (10 credits)
• Bioinspired Computing:
Focuses on modern AI techniques inspired by biological systems, including evolutionary algorithms, neural computing, and agent-based models. (10 credits)
• Cyber Security Team Project:
Equips students with knowledge and skills for securing organizations from cyber threats and responding to breaches. (10 credits)
• Cognitive and Biomimetic Robotics:
Explores progress in developing cognitive and biomimetic robots, relating AI, machine learning, and cognitive science to robotic systems. (10 credits)
• Software Testing and Analysis:
Introduces techniques for analyzing and testing software systems, covering static analysis, dynamic testing, test automation, and code assessment. (10 credits) Covers project life cycle, team management, and roles and responsibilities. (10 credits)
• Computer Vision:
Introduces computer vision, covering image processing, detection and recognition, semantic understanding, and vision and video analysis. (10 credits)
• Software Development for Mobile Devices:
Provides a grounding in software development for mobile devices using the Android platform. Emphasizes object-oriented programming principles and design patterns. (10 credits)
• Advanced Algorithms:
Focuses on efficient algorithm design and analysis for solving complex computational problems. Covers optimization tools, algorithmic techniques, and modern computational models for massive datasets. (10 credits)
• Undergraduate Ambassadors Scheme in Computer Science:
Provides an opportunity for students to gain experience in computer science secondary education through a mentoring scheme with a local school teacher. (20 credits)

Assessment:

• Assessment Methods:
• Exams/tests
• Coursework
• Practical sessions
• Dissertation (Year 3)
• Project presentations (Year 3)
• Assessment Criteria:
• Demonstrated understanding of concepts and theories
• Application of knowledge to practical problems
• Ability to analyze and solve problems
• Communication skills (written and oral)
• Teamwork and collaboration skills

Teaching:

• Teaching Methods:
• Lectures
• Practical sessions
• Tutorials
• Seminars
• Independent study
• Group work
• Project-based learning
• Faculty:
• The program is taught by experienced and internationally recognized computer scientists.
• Many lecturers are actively involved in research, ensuring that the curriculum is up-to-date and relevant.
• Unique Approaches:
• The program emphasizes a research-led education, encouraging creativity and curiosity.
• Students gain hands-on practical skills and industry experience through real-world projects and guest lectures from industry professionals.
• The program incorporates ethical considerations and environmental impact into the curriculum.

Careers:

• Potential Career Paths:
• IT consultant
• Software engineer
• Software developer
• Project manager
• Data scientist
• Research scientist
• Opportunities:
• Graduates are highly sought after by leading companies in the technology sector.
• The program provides opportunities for internships and placements, enhancing career prospects.
• Outcomes:
• Graduates are well-equipped for successful careers in the rapidly evolving field of computer science and artificial intelligence.

Other:

• The program is accredited by the British Computer Society (BCS).
• The program offers opportunities for study abroad and placement years.
• The University provides access to state-of-the-art facilities, including dedicated computer labs, a robotics arena, and virtual reality facilities.
• The program is designed to develop students' transferable skills, such as communication, teamwork, and problem-solving.
• The University of Sheffield is committed to providing a supportive and inclusive learning environment.

Please use 2024-25 information as a guide. £9,250Home students 2024 annual tuition fee £29,110Overseas students 2024 annual tuition fee