Bachelor’s in Computer Science with Specialization in AI & ML

Credit

180 ECTS

Accredited By

Woolf University Malta

Intakes

Sep | Oct | Jan | Feb

Duration

3 Years

Building Intelligent Solutions for a Data Driven World

The three-year undergraduate programme in Computer Science with a concentration in Artificial Intelligence and Machine Learning awarded by Woolf University Malta and delivered in partnership with Exeed ECX provides students with a strong foundation in computer science alongside specialised expertise in AI and ML. The curriculum equips learners with the knowledge and skills needed to solve complex problems and develop innovative solutions across a wide range of industries.

Students gain proficiency in core areas such as mathematics, data science, programming, database management, and emerging AI technologies. They learn to analyse and visualise data, design and implement algorithms, and use modern tools and platforms to build intelligent systems.

The programme emphasises project-based learning, enabling students to apply theoretical knowledge to real-world challenges through individual and team projects. Comprising a total of 29 modules, the programme prepares graduates for successful careers in the rapidly evolving field of artificial intelligence and machine learning.

Why Choose This Program?

This undergraduate programme in Computer Science with a concentration in Artificial Intelligence and Machine Learning combines strong computing fundamentals with in demand AI expertise. Through hands on, project based learning and an industry relevant curriculum, students gain practical skills to design intelligent systems, solve real world problems, and pursue careers or advanced studies in the rapidly evolving field of AI.

Here are the five primary reasons to choose this program:

Specialised Focus on AI & Machine Learning
Strong Computer Science Foundation
Hands On, Project Based Learning
Industry Relevant Curriculum
Pathway to Advanced Studies

Programme Structure & Curriculum

The Bachelor of Business Economics and Management is structured across Levels 4, 5, and 6, ensuring a progressive development from core foundations to advanced strategic and leadership capabilities.

Year 1:

Tier 1 – Foundational & Industry Experience – 60 Ects

Module	Module Name	ECTS	Credit
1	Optimizing Your Learning: Academic & Critical Thinking Skills	3	6

This module develops students’ critical thinking, academic writing, and research skills. Students learn to evaluate arguments, analyse evidence, write clearly and ethically, and use research tools effectively. By the end of the module, they will be able to communicate ideas clearly, analyse complex problems, and produce high-quality academic and professional work.

Learning Outcomes

Define critical thinking and its role in academic writing
Describe the key characteristics of effective academic writing

Module	Module Name	ECTS	Credit
2	Communicating for Success	3	6

This module develops essential written, verbal, and non-verbal communication skills for academic and professional success. Through close reading, structured writing, presentations, and continuous feedback, students learn to communicate clearly, confidently, and effectively. The module culminates in a project focused on presenting a technical topic to a non-technical audience.

Learning Outcomes

Demonstrate effective writing structure, grammar, audience awareness, and oral presentation skills
Develop close reading, written, verbal, and non-verbal communication skills

Module	Module Name	ECTS	Credit
3	Mathematical Thinking	6	12

This module introduces core concepts of linear algebra and calculus and their applications in AI. Students develop logical and mathematical reasoning skills, learn to analyse data, build mathematical models, and optimise algorithms, providing a strong foundation for advanced study in algorithms and discrete mathematics.

Learning Outcomes

Apply logical and mathematical reasoning for advanced algorithmic study
Perform algebraic operations required for programming tasks
Demonstrate effective use of mathematical concepts in computational applications

Module	Module Name	ECTS	Credit
4	Operating Systems	3	6

This module introduces the principles and functions of operating systems, including Windows, macOS, and Linux. Students learn about operating system architecture, core components, and system interaction through APIs, preparing them for advanced AI and ML application development.

Learning Outcomes

Classify major types of operating systems
Explain key operating system functions, including process and memory management

Module	Module Name	ECTS	Credit
5	Computer Systems: Computer System Architecture	6	12

This module introduces core concepts of computer system architecture, including instruction set architecture (ISA), memory systems, and instruction formats. Students learn to analyse system design choices and understand their impact on performance, providing a strong foundation for advanced study in computer science and engineering.

Learning Outcomes

Explain instruction set architecture and its role in system design
Identify modern memory systems used in computer architecture
Classify common instruction formats and their performance implications

Module	Module Name	ECTS	Credit
6	Database Management	6	12

This module introduces database management systems, focusing on database design, normalization, and optimization. Students gain hands-on experience using SQL to manage and retrieve data, building a strong foundation for AI, ML, and industry-based database applications.

Learning Outcomes

Identify major types of database management systems, including relational and NoSQL
Describe key components of database management systems, such as tables and indexes

Module	Module Name	ECTS	Credit
7	Programming 1	6	12

This course introduces programming as a problem-solving tool through a project-based approach. Students develop algorithmic thinking and learn Python fundamentals, core data structures, and basic web application development using Flask. Collaborative projects reinforce industry practices and practical coding skills.

Learning Outcomes

Apply programming fundamentals to solve problems independently
Develop complex programs using data structures and algorithmic thinking
Design effective solutions using functions, loops, and iterations

Module	Module Name	ECTS	Credit
8	Web Application Development: Web Programming	6	12

This module introduces web application development using HTML, CSS, and JavaScript. Students learn how these technologies work together to build interactive, well-structured web applications and translate design specifications into functional solutions through hands-on projects.

Learning Outcomes

Explain the roles of HTML, CSS, and JavaScript in web development
Identify common web application architectures
Develop functional web applications based on design specifications

Module	Module Name	ECTS	Credit
9	Fundamentals of AI And ML	6	12

This module introduces core concepts of Artificial Intelligence and Machine Learning, including key algorithms and real-world applications. Students gain practical experience in selecting, training, and evaluating machine learning models to solve data-driven problems.

Learning Outcomes

Define artificial intelligence and machine learning
Identify major types of machine learning algorithms
Select appropriate machine learning algorithms for specific problems

Module	Module Name	ECTS	Credit
10	Emerging Technologies in AI	3	6

This module explores emerging technologies in Artificial Intelligence, focusing on their applications, societal and industrial impact, and ethical considerations, preparing students to make informed decisions in future AI-related roles.

Learning Outcomes

Identify emerging AI technologies such as deep learning and natural language processing
Describe their impact on society and industry

Module	Module Name	ECTS	Credit
11	Industry Experience 1	12	24

This module provides practical exposure to the use of Artificial Intelligence across industries, enabling students to identify real-world applications and effectively communicate AI concepts to non-technical stakeholders.

Learning Outcomes

Identify industries applying AI, including healthcare, finance, and manufacturing
Recognise key AI applications across sectors
Explain AI concepts clearly to non-technical audiences

Year 2:

Tier 2 – Foundational & Industry Experience – 60 Ects

Module	Module Name	ECTS	Credit
1	Discrete Math	6	12

This course builds on mathematical foundations essential to computer science, covering logic, combinatorics, probability, set theory, graph theory, and number theory. Emphasis is placed on practical applications in algorithms, data science, machine learning, and cryptography, culminating in a group project exploring discrete mathematics concepts.

Learning Outcomes

Write precise, readable code informed by discrete mathematics
Apply core discrete mathematics concepts in computer science contexts

Module	Module Name	ECTS	Credit
2	Engineering for Development	6	12

This course explores how technology can address global social and economic challenges using the UN Sustainable Development Goals as a framework. Students analyse root causes of development issues and design effective, sustainable technology solutions.

Learning Outcomes

Apply planning, resource, and team management skills
Decompose complex problems into actionable engineering solutions
Analyse development challenges and propose effective technology-based responses

Module	Module Name	ECTS	Credit
3	Network and Computer Security: Computer Networks	6	12

This module introduces computer networking principles, including network architectures, protocols, and models. Students gain hands-on experience in designing, configuring, and troubleshooting networks.

Learning Outcomes

Design and implement networks using appropriate topologies and protocols
Use network monitoring tools to optimise performance
Select suitable network models for specific applications

Module	Module Name	ECTS	Credit
4	Data Structures and Algorithms I	6	12

This course introduces fundamental data structures and algorithms, focusing on implementation, optimisation, and performance analysis to support advanced study in data science and machine learning.

Learning Outcomes

Implement and analyse basic data structures
Develop efficient searching and sorting algorithms
Evaluate algorithm performance using time and space complexity

Module	Module Name	ECTS	Credit
5	Python for Machine Learning	6	12

This module develops practical skills in using Python and key libraries for machine learning, covering data preprocessing, model training, and result interpretation through hands-on projects.

Learning Outcomes

Implement machine learning algorithms using Python libraries
Perform data preprocessing and feature engineering
Analyse and interpret machine learning results

Module	Module Name	ECTS	Credit
6	Exploratory Data Analysis and Visualization	6	12

This module focuses on analysing datasets to identify patterns and trends and communicating insights through effective data visualisation techniques.

Learning Outcomes

Apply exploratory data analysis methods
Present insights using appropriate visualisation tools
Select suitable visualisation techniques for different datasets

Module	Module Name	ECTS	Credit
7	Challenge Studio 1	6	12

In this project-based module, students work in teams to design and prototype technology solutions to real-world development challenges using human-centred design principles.

Learning Outcomes

Develop a prototype or minimum viable product as a team
Apply user research and iterative design methods
Measure impact and refine solutions

Module	Module Name	ECTS	Credit
8	Cyber Security Fundamentals	6	12

This module introduces core cybersecurity concepts, threats, and defence mechanisms, including incident detection, response, and reporting.

Learning Outcomes

Identify common cyber threats and attacks
Explain key cybersecurity principles and best practices
Apply basic security tools and incident response procedures

Module	Module Name	ECTS	Credit
9	Industry Experience 2	12	24

This module provides hands-on industry exposure through real-world projects, allowing students to apply theoretical knowledge to practical AI and ML challenges.

Learning Outcomes

Apply academic concepts to industry problems
Analyse data and communicate findings to stakeholders
Evaluate the feasibility and effectiveness of proposed solutions

Year 3:

Tier 3 – Advanced & Applied Project – 60 Ects

Module	Module Name	ECTS	Credit
1	Ethics and Social Implications of AI	3	6

This course examines ethical issues in AI, including bias, privacy, and accountability, and explores AI’s social and economic impacts on individuals, organisations, and society.

Learning Outcomes

Explain key ethical challenges in AI, including bias and privacy
Analyse the social and economic impacts of AI adoption

Module	Module Name	ECTS	Credit
2	Digital Marketing and Analytics	6	12

This module introduces digital marketing strategies and analytics tools to plan, execute, and evaluate online marketing campaigns across multiple digital channels.

Learning Outcomes

Use analytics tools to optimise digital marketing campaigns
Develop effective social media marketing strategies
Evaluate campaign performance to improve brand engagement

Module	Module Name	ECTS	Credit
3	Natural Language Processing Fundamentals	6	12

This module covers core NLP concepts, techniques, and machine learning models, enabling students to design and apply NLP solutions to real-world problems.

Learning Outcomes

Apply core NLP techniques such as tokenization and tagging
Evaluate NLP models for tasks like sentiment analysis and NER
Design basic NLP solutions for practical applications

Module	Module Name	ECTS	Credit
4	Computer Vision Fundamentals	6	12

This course introduces image processing and computer vision techniques, focusing on object detection, feature extraction, and model evaluation.

Learning Outcomes

Interpret results of image processing techniques
Evaluate feature extraction methods
Compare object detection algorithms for real-world use

Module	Module Name	ECTS	Credit
5	Interaction Design	6	12

This project-based course explores human–computer interaction principles, usability evaluation, and multi-platform design for emerging technologies.

Learning Outcomes

Design, prototype, and test user-centred interfaces
Apply HCI principles to solve interaction design problems
Evaluate usability and improve interface design

Module	Module Name	ECTS	Credit
6	Backend Development	6	12

This module develops server-side development and DevOps skills for building secure, scalable, enterprise-level applications.

Learning Outcomes

Design and secure APIs for web and mobile applications
Implement modern DevOps workflows and CI/CD pipelines
Apply best practices in backend architecture and testing

Module	Module Name	ECTS	Credit
7	Applied AI & ML Project Management	6	12

This module focuses on managing AI and ML projects, combining project management principles with applied machine learning practice.

Learning Outcomes

Plan AI/ML projects with timelines, risks, and dependencies
Apply ML techniques to real-world problems
Manage project resources and team performance effectively

Module	Module Name	ECTS	Credit
8	Capstone Research Methods	6	12

This course prepares students for their capstone project by developing research, planning, and proposal-writing skills.

Learning Outcomes

Plan and propose a capstone research project
Apply qualitative and quantitative research methods
Address ethical and methodological considerations in research

Module	Module Name	ECTS	Credit
9	Applied Computer Science (Capstone Project)	15	30

This capstone enables students to design, implement, and present an applied computing solution, integrating technical, ethical, and professional skills.

Learning Outcomes

Design and implement an applied AI or computing solution
Evaluate the effectiveness and impact of the solution
Present project outcomes to academic and industry stakeholders

Overview

Tuition Fees

Entry Requirements

Financial Support

Bachelor Degree

The course provides students with in depth and specialised knowledge in computer science while fostering strong critical thinking and strategic planning abilities needed to navigate rapidly changing and fast-paced environments. It emphasises technological and operational analysis, enabling students to evaluate complex systems and make informed decisions.

Tuition Fees

The total tuition fees for the BBA (Honours) in Tourism & Hospitality Management:

Year 1: 1200 £
Year 2: 1200 £
Year 3: 1600 £

Tuition fees: 4000 £

Application fee: 100 £ (non-refundable)

* This price includes all online program related costs. Additional costs may apply in some circumstances.

Entry Requirements

These qualifications are intended for individuals aged 18 and above. Applicants should typically meet at least one of the following criteria:

Possession of a relevant Level 3 Diploma or an equivalent qualification
Completion of GCE A-levels in two subjects or an equivalent academic achievement
Learners should have a suitable level of English language proficiency
LCHS ensures learners are appropriately assessed and placed to support successful completion of the programm.

Financial Support

We understand that funding your education can be challenging, which is why we provide convenient instalment plans to support you. These flexible payment options help you manage your tuition fees comfortably while staying focused on your learning journey.

By offering flexible payment schedules, we ensure that learners can access high‑quality education without compromising their financial stability. For further information on setting up a flexible payment plan, please connect with our admissions team. They look forward to supporting you as you begin your chosen programme.

WOOLF UNIVERSITY, MALTA

Woolf, based in Malta, is transforming higher education through an innovative collegiate university model. As the world’s first global collegiate higher education institution, Woolf allows qualified organizations to operate as accredited member colleges.

Its mission is to expand access to high-quality, internationally recognized education that is both transferable and valued worldwide. Woolf upholds strong academic standards and promotes humane, democratic, and international values. With globally recognized ECTS credits, students can be assured that their qualifications hold real value and are recognized by employers across the world.

Accreditations

While individual degree programs are accredited through the designated legal entities listed below, all programs offered through Woolf adhere to the same rigorous quality assurance standards. These standards are managed through Woolf’s proprietary Accreditation Management System (AMS), ensuring consistency and academic integrity across all member colleges.

Woolf is a registered and trademarked organization with the following authorities:

United States Patent and Trademark Office (USPTO), United States
European Union Intellectual Property Office (EUIPO)
Intellectual Property India (IPIndia)

Apply Request Programme Details Today

Career Opportunities After Graduation

Graduates of the Bachelor’s in Computer Science with Specialization in AI & ML can pursue roles such as:

Machine Learning Engineer
AI Engineer
Data Scientist
Applied AI Engineer
MLOps Engineer (Junior)

Data Analyst
Business Intelligence (BI) Analyst
Data Engineer (Entry-Level)
Robotics / Intelligent Systems Engineer
Product Analyst / AI Product Associate

This degree also provides a strong foundation for advanced studies, entrepreneurship, or cross-disciplinary roles combining AI with business, design, or engineering.

Teaching & Learning Approach

Interactive online learning

Applied projects and case-based assignments

Research-driven assessments

Practical business and industry scenarios

Continuous academic guidance and support

Assessment Method

Internally assessed modules

Criteria-based evaluation aligned with learning outcomes

Focus on assignments, projects, and applied research

No rote memorisation, emphasis on real-world skills

Who Should Enroll?

A Bachelor’s Degree in Computer Science with a specialization in AI & ML is ideal for individuals who:

Are interested in technology and problem-solving and want to understand how intelligent systems are designed and built.

Have a curiosity about Artificial Intelligence and Machine Learning, including how data-driven systems make predictions and decisions.

Aspire to careers in AI, data science, software engineering, or emerging technology fields.

Want practical, hands-on learning, including programming, data analysis, and real-world AI projects.

Are motivated to work in fast-evolving, innovation-driven industries such as healthcare, finance, automation, cybersecurity, or smart systems.

Wish to combine technical skills with ethical, social, and business awareness in the responsible development and use of AI technologies

In short, this program is well-suited for students who want to build intelligent solutions, analyze complex data, and shape the future of technology using AI and Machine Learning.

Academic Support

Students are enrolled in a European degree program that combines blended teaching methodologies with extensive academic support delivered by faculty of international standards in business management.

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