BSc in Computer Science and Artificial Intelligence

The BSc in Computer Science and Artificial Intelligence is a comprehensive and dynamic program blending foundational computer science principles with cutting-edge advancements in Artificial Intelligence. The curriculum is designed to equip students with a foundational understanding of computer science, mathematics, and AI concepts, specialised programming skills, and problem-solving abilities. It enables specialization in specific AI domains, fosters ethical considerations in AI development, and prepares students for the dynamic and evolving job market in industries such as technology, healthcare, governance, education, and many more, all experiencing a growing demand for AI expertise.

Amongst other industry linkages, the program is supported by JetBrains, one of the top 100 IT companies in the world. It is a hands-on program enabling students to master the mathematical and analytical foundations underpinning Artificial Intelligence.



240 ECTS




Objective of the Bachelor (BSc) in Computer Science and Artificial Intelligence

Students applying for this new comprehensive undergraduate program embark on an exciting journey into the world of cutting-edge technology, focusing on Information Technology, Robotics, Machine Learning, Cybersecurity, and Theoretical Computer Science. Designed to empower students with a solid foundation in computer science, mathematics, and modern IT, the BSc in Computer Science and Artificial Intelligence program equips students with the skills needed to excel in today’s competitive tech landscape.

This program aims to provide undergraduate education and scientific specialisation in Computer Science and Artificial Intelligence, helping students acquire professional knowledge and a broader education in this field. The program is innovative and multidisciplinary, offering a unique blend of theory and practice, immersing students in real-world projects that span various mathematical and technological domains.

In collaboration with industry leaders such as JetBrains, we offer numerous scholarships to our students. The program is supported by a dedicated expert faculty, with vital research activities and a dynamic presence in AI, is equipped with new state-of-the-art facilities and laboratories, and driven by a focus on innovation. This makes the program poised to nurture the next generation of skilled and forward-thinking professionals in this dynamic field.

We have an MOU with over 20 companies, and industry leader JetBrains, heads our Business Advisory Board. Students have the opportunity to undertake placements and internships and engage in numerous extracurricular activities and workshops offered by our collaborators. The program has adopted placement as a compulsory course, emphasizing the practical application of knowledge. The academic supervisor of the program oversees this process, and the evaluation of the student’s performance during a placement is carried out exclusively by the hosting company.

Throughout their study journey, students receive support from a personal academic advisor who monitors their progress and assists with any issues that may arise. Additionally, students can choose to be mentored by one of the companies participating in the Program’s Business Advisory Board. These mentors guide students to become ideal candidates for employment upon completion of their studies. They also suggest elective courses, assignment topics, and offer thesis topics, among other support.


Graduates of the BSc in Computer Science and Artificial Intelligence program can pursue a variety of employment roles, such as AI Engineer, Data Engineer, Data Analyst, Robotics Engineer, AI Researcher, Computer Vision Engineer, and many more in both the private and public sectors. Graduates can also choose to continue their studies to obtain a Master’s or Ph.D. degree, join research laboratories to contribute to cutting-edge discoveries, or launch their own startup ventures in the technology sector.


The BSc in Computer Science and Artificial Intelligence is designed based on the latest recommendations from two leading international scientific organizations: the Association for Computing Machinery (ACM) and the IEEE Computer Society (CS). It complies fully with the requirements and scope of the European Standards and Guidelines on Quality Assurance.

As part of this program, students will comprehensively grasp the fundamentals of mathematics and computer science, forming the bedrock for modern software, artificial intelligence, and robotics. They will engage in innovative design by creating and developing software, hardware architectures, operating systems, and distributed systems, applying appropriate design methods and tools. The curriculum includes the construction of programming languages, compilers, interpreters, virtual machines, and frameworks to facilitate effective software development processes. Emphasizing the practical application of AI techniques, students will enhance search engines, social networks, and intelligent assistants, while also delving into robotics applications for autonomous robots, the Internet of Things (IoT), and various domains. Proficiency in machine learning and deep learning algorithms, spanning computer vision, natural language processing, reinforcement learning, and recommendation systems, is a key focus. Additionally, the program equips students with the skills to establish and manage IT businesses, emphasizing effective team collaboration, process management, and customer and partner relations. Moreover, students will demonstrate advanced critical thinking skills in AI, considering legal, social, ethical, and professional aspects to formulate informed decisions and recommendations.

In collaboration with McGraw Hill Publishing, we have adopted Adaptive Learning methods to provide customised learning experiences for individual students within the courses. Utilising AI and data analytics, adaptive learning systems continuously assess a student’s performance and dynamically adjust the learning content, pace, and activities in real-time to suit each student’s specific needs and abilities.

A variety of summative and formative assessment methods are used in the program, such as oral presentations and demonstrations, lab activities, peer assessment, simulation games, case studies, jigsaws, quizzes, and more. These methods allow students to integrate the skills acquired and assess and report on the success of their solutions. Some assessments will be partially or wholly group-based, providing students with the experience of team-based work. The evaluation typically includes final written exams (50%) and midterm assessments (50%), which may consist of written exams, assignments, and interactive activities. To secure a passing grade, students need to achieve 40% or higher in both the midterm assessments and the final exams.



CodeCourse titleCourse typeECTS
CSAI111Analysis for Machine Learning 1: Differential Calculus and ApplicationsCompulsory6
CSAI112Discrete Mathematics 1: Logic and CombinatoricsCompulsory6
CSAI113Linear AlgebraCompulsory6
CSAI114Computer Science Basics with PythonCompulsory6
CSAI115Programming Basics with CCompulsory6


CodeCourse titleCourse typeECTS
CSAI121Analysis for Machine Learning 2: Integral Calculus and ApplicationsCompulsory6
CSAI122Discrete Mathematics 2: Discrete Probability and Graph TheoryCompulsory6
CSAI123Algorithms 1: Basic ToolboxCompulsory6
CSAI124Programming ParadigmsCompulsory6
CSAI125ANN1: Introduction to Neural NetworksCompulsory6


CodeCourse titleCourse typeECTS
CSAI231Pattern Recognition and Machine LearningCompulsory6
CSAI232Continuous Probability TheoryCompulsory6
CSAI233Algorithms 2: Data StructuresCompulsory6
CSAI234Computer ArchitectureCompulsory6
CSAI235Algorithm EngineeringCompulsory6


CodeCourse titleCourse typeECTS
CSAI241Theoretical Computer ScienceCompulsory6
CSAI242Optimisation for Machine LearningCompulsory6
CSAI243Project-based Exploration of Modeling and SimulationCompulsory6
CSAI244Human Computer InteractionCompulsory6
CSAI245Data Science and Big DataCompulsory3


CodeCourse titleCourse typeECTS
CSAI352Agile Scrum for AI DevelopmentCompulsory6
CSAI353Data MiningCompulsory6
CSAI354ANN2: Deep and Reinforcement LearningCompulsory6
*Elective 1Elective6


CodeCourse titleCourse typeECTS
CSAI361Natural Language Processing and Foundational ModelsCompulsory6
CSAI362Artificial Intelligence LabCompulsory6
CSAI363Robotics and Computer VisionCompulsory6
*Elective 2Elective6
*Elective 3Elective6


CodeCourse titleCourse typeECTS
CSAI471Mobile Applications in KotlinCompulsory6
CSAI472AI-Enhanced Cybersecurity: From Theory to PracticeCompulsory6
*Elective 4Elective6
CSAITHE01Thesis ICompulsory3
CSTHERMResearch MethodsCompulsory3
LCS01Language for ScienceCompulsory6


CodeCourse titleCourse typeECTS
CSIEIndustrial Experience (Placement)Compulsory6
CSAI481Responsible AI: Ethical and Legal ConsiderationsCompulsory6
*Elective 5Elective6
CSAITHE02Thesis IICompulsory12


CodeCourse titleCourse TypeECTS
CSE01Introduction to Innovation and EntrepreneurshipElective6
CSE03Distributed Ledger TechnologiesElective6
CSE06Game Design & DevelopmentElective6
CS242Operating SystemsElective6
CSE08IoT Networks and ProtocolsElective6
CSE09Cyber Crime and Legal ConsiderationsElective6
PSYC100Introduction to PsychologyElective6
CSE12Analysis and Design of Information SystemsElective6
CSE13Network ManagementElective6
CSE14Computer Architecture IIElective6
CSE15Fine-grained ComplexityElective6


The University reserves its right to define the electives offered on an academic year basis.

The programme structure may change without prior notice, as a result of quality assurance procedures or/and programme recertification.


Register your interest and one of our admissions consultants will contact you with guidance and additional information.


Savvas Chatzichristofis

Professor of Artificial Intelligence

Head of the Department of Computer Science

Coordinator of the Bachelor in Applied Computer Science

Savvas A. Chatzichristofis pursued the Diploma and the Ph.D. degree (with honors) both from the Department of Electrical and Computer Engineering, Democritus University of Thrace, Greece...

Avgousta Kyriakidou Zacharoudiou

Assistant Professor in Software Project Management

Coordinator of the Bachelor in Computer Science and Artificial Intelligence

Dr Avgousta Kyriakidou Zacharoudiou is an Assistant Professor in Software Project Management at Neapolis University Cyprus. Prior to joining Neapolis University she was an Associate Professor in Computing at the University of Greenwich London, UK since 2011....

Zach Anthis

Lecturer Artificial Intelligence and Data Analytics (AIDA)

Zach is a pure mathematician admittedly turned computer scientist. He holds a PhD in Artificial Intelligence and Data Analytics with integrated MSc in Quantitative Methods, from the Department of Culture, Communication, and Media at University College London (UCL)....

Lefteris Zacharioudakis

Assistant Professor of Cybersecurity

Dr. Zacharioudakis Lefteris received his BSc (1997), MSc (1999) and PhD (2018) degree from National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute"....

Elena Kakoulli

Lecturer in Information Systems

Coordinator of the MSc in Information Systems and Digital Innovation (conventional and Distance)

Dr Elena Kakoulli acquired a Ph.D. degree in Computer Engineering at the Department of Electrical and Computer Engineering and Informatics of the Cyprus University of Technology. She holds a Master’s degree in Computer Science from the University of Cyprus...

Dmitrii Botov

Assistant Professor in Generative Artificial Intelligence and Machine Learning

Dmitrii Botov graduated (with honors) in Computer Engineering from South Ural State University (Russia) in 2010. He successfully defended PhD thesis in Artificial Intelligence about natural language processing of short texts in 2019...

Andrei Smolenskii

Assistant Professor of Algebra

Dr. Andrei Smolensky received his Specialist Diploma in Mathematics in 2012 at Saint Petersburg State University and his PhD in Algebra in 2016 at St. Petersburg Department of V.A. Steklov Mathematical Institute...

Loukia Taxitari

Lecturer in Research Methods

Loukia Taxitari is a Lecturer in Research Methods in the Department of Psychology at Neapolis University. In 2009, she received a PhD in experimental psychology and, in 2004...

Marios Poullas

Visiting Lecturer of Digital Innovation

Marios Poullas has a background in Biosciences and holds an MRes, a PhD, and an MBA. Over the past 6+ years, Marios has been a driving force in promoting digital transformation and sustainability in businesses across various high-tech sectors....

Panayiotis Christodoulou

Visiting Lecturer of Computer Science

Panayiotis Christodoulou holds a PhD in Computer Engineering and Informatics from the Cyprus University of Technology (CUT). He completed his undergraduate and postgraduate studies at the Manchester University, UK (MEng) and the Frederick University...

Michael Georgiades

Assistant Professor of Computer Networks

Coordinator of the MSc in Data Analytics and Financial Technology

Dr Michael Georgiades is an Assistant Professor He holds a BEng degree in Communications and Radio Engineering from King’s College London in 2000 (First Class Honours), an MSc degree in Telecommunications at University College London in 2001 and a PhD in Wireless and Mobile Networks from University of Surrey in 2008..

Lina Efthyvoulou

Lecturer in Counselling Psychology

Dr Lina Efthyvoulou is a registered Chartered Counselling Psychologist, a Lecturer in Counselling Psychology and Supervisor at the Department of Psychology, School of Health Sciences, Neapolis University Pafos.
A modern BSc programme in Applied Computer Science enriched with interdisciplinary courses from the fields of Economics, Finance, and Business