BSc (Hons) Computer Science with AI

Course Goals

• Explore artificial intelligence and its relationship to machine learning, parallel programming, and data science, and learn how these areas drive innovation and change in the domain of computer science.
• Master the practical skills and theoretical knowledge to develop software solutions that address demanding user expectations and complex customer requirements.
• Gain industry-relevant experience as you apply real-world, software development practices within teams of your peers, preparing you for your AI specialist career after graduation.  

Fee Structure

Degree Highlights

• Programming proficiency in various languages
• Focus on data science with large datasets
• Development of transferable skills
• Career-ready with a holistic skill set

Eligibility Criteria

Minimum 2.4 GPA in 10+2 level or 3.5 Credit in A level (Science / Management / Humanities)

English Requirement

The students with valid IELTS Report Forms with a 6.0 overall band or equivalent are eligible to apply.

• AI Software Developer
• Data Scientist
• Software Engineer
• System Architect
• AI Researcher
• Project Manager
• Data Engineer

Year One

Semester I

• Programming: Concepts and Algorithms
  • An introduction to programming, algorithmic problem solving, version control, and testing. Covers recursive functions, error handling, data structures (such as arrays and associative arrays), and their applications in problem-solving. Additionally, includes studies on Boolean logic, fundamental algorithmic complexity, and differentiating various concepts. Programming languages, classification of errors
• Mathematical Skills for Computing Professionals
  • Overview of software design principles, Agile methodologies, design patterns such as Factory, Proxy, and Singleton, version control, unit and integration testing, test-driven development (TDD), behavior-driven development (BDD), UML, and the utilization of RESTful APIs.
• Computer Systems
  • Provides students with a comprehensive understanding of database management systems, data modeling, and design principles. Learners will acquire practical experience with popular database management systems and explore how to design, implement, and manage databases effectively.

Semester II

• Programming: Professional Practice
  • Develop and understand algorithms to solve problems while measuring and optimizing algorithm complexity. Work with storage technology, apply statistical analysis to draw meaningful conclusions and use machine learning tools to discover hidden patterns.
• Working with Data
  • An introductory course on data collection, cleaning, transformation, visualization, and analysis. Use spreadsheets, SQL, and Python tools to learn how to manage real-world datasets and make data-driven decisions. 
• Integrative Project  
  • A capstone course where knowledge and skills are utilized to address real-world problems. Create a comprehensive project that integrates concepts from various disciplines.

Year Two

Semester III

• Software Engineering
  • Requirements Engineering, Software Design, Programming and coding, Testing and QA, Software project management, Software maintenance and evolution, Software Documentation
• Theory of Computation
  • Automata Theory, Turing Machines, Decidability and Undecidability, Formal Language Theory, Computational Complexity, Applications of Computational Theory
• Advanced Algorithms
  • Advanced structures (Trees, graphs, and heaps), Algorithm Design and Techniques (divide-and-conquer algorithms, Dynamic programming and memorization, greedy algorithms, backtracking, and branch-and-bound techniques), Graph Algorithms, Advanced sorting and searching, Complexity and Analysis.

Semester IV

• Operating Systems, Security, and Networks
  • This section addresses core operating system functions and their role in implementing security features, safeguarding against threats, and maintaining system integrity through various mechanisms.
• Data Science
  • The Data Science module extends your data skills by introducing Big Data concepts and advanced tools, including predictive modeling and data visualization, to help you clearly communicate analysis results.
• Artificial Intelligence
  • This section addresses the fundamental concepts, techniques, and applications of AI. Students will explore machine learning, neural networks, natural language processing, and computer vision. 

Year Three

Semester V

• Project Discovery
  • Identify and refine a project topic and research question, conduct an initial literature review, and create a detailed, achievable project plan. Consider the research's social, legal, and ethical impacts.
• Machine Learning
  • Offers a comprehensive approach to machine learning by integrating theory and practical application using Python tools and reliable data. Students acquire hands-on experience and insight into topics such as linear and logistic regression, support vector machines, decision trees, and model evaluation. Furthermore, it addresses unsupervised learning, the bias-variance tradeoff, and the ethical considerations in machine learning.
• Robotics and Intelligent Agents
  • Delves into core AI concepts, including search algorithms, knowledge representation, and planning. Students gain hands-on experience applying these techniques to problem-solving tasks. The curriculum also covers probabilistic reasoning and decision-making in uncertain scenarios.

Semester VI

• Artificial Neural Networks
  • Provides a comprehensive overview of artificial neural networks, focusing on their core concepts and real-world applications. Students will learn to design and implement neural network models. Topics covered include various network types, data handling, deep learning, and their applications in fields like vision, speech, and robotics. The course also discusses neural network simulators, limitations, and emerging trends.
• Security
  • Conduct in-depth research on a computer science topic culminating in a technical project and written report; conduct supervisor meetings to review progress.
• Dissertation and Project Artefact
  • Introduces fundamental security concepts such as cryptography, infrastructure security, and secure programming. Students learn to analyze systems and create safe environments. The content covers cryptography (ciphers, hashes, PKI, digital signatures), infrastructure security (policies, network security, audits), and secure development (defensive coding).