Technology

Master of Science in Artificial Intelligence

The Master of Science in Artificial Intelligence is a postgraduate programme that prepares professionals for advanced practice in AI, machine learning, and data science. The programme combines AI theory with practical machine learning and intelligent systems application.

Core areas include machine learning, deep learning, natural language processing, computer vision, robotics, AI ethics, data science, intelligent systems, research methods, and thesis. Students engage with both AI theory and practical intelligent systems development through coursework and research.

The programme is offered by JKUAT, Open University of Kenya, and Kabarak University. JKUAT and OUK are public universities while Kabarak is private. All offer the programme over two academic years through full-time, part-time, or blended modes of study.

Students develop competencies in machine learning, deep learning, NLP, computer vision, robotics, AI ethics, data science, and research methods. The programme includes coursework, examinations, and a research thesis or project.

OUK charges approximately KES 93,750/year (KES 187,500 total). JKUAT charges approximately KES 254,100/year (KES 508,200 total). Kabarak charges approximately KES 160,000/year (KES 320,000 total). Entry requires a Bachelor's degree with Second Class Honours Upper Division in computer science, IT, data science, mathematics, or related fields from a recognised university.

Graduates pursue careers as AI engineers, machine learning engineers, data scientists, NLP engineers, computer vision engineers, and AI lecturers across technology companies, financial institutions, research organisations, and academic institutions.

Skills Required

Machine Learning and Model Development
Deep Learning and Neural Networks
Natural Language Processing and Text Analytics
Computer Vision and Image Processing
Robotics and Autonomous Systems
AI Ethics and Responsible AI
Data Science and Big Data Analytics
Intelligent Systems and Knowledge Representation
Research Methods in AI
Academic Writing and Thesis Research

Key Subjects

Machine Learning and Model Development
Deep Learning and Neural Networks
Natural Language Processing and Text Analytics
Computer Vision and Image Processing
Robotics and Autonomous Systems
AI Ethics and Responsible AI
Data Science and Big Data Analytics
Intelligent Systems and Knowledge Representation
Research Methods in AI
Thesis Research

Certifications

Google AI Certification
AAAI AI Professional Certification

Specializations

Machine Learning

Focuses on machine learning, covering supervised learning, unsupervised learning, reinforcement learning, model evaluation, and managing machine learning.

Deep Learning

Examines deep learning, covering neural networks, CNNs, RNNs, transformers, transfer learning, and managing deep learning.

Natural Language Processing

Covers NLP, covering text processing, language models, sentiment analysis, machine translation, chatbots, and managing NLP.

Computer Vision

Focuses on computer vision, covering image processing, object detection, facial recognition, scene understanding, and managing computer vision.

Robotics and Autonomous Systems

Examines robotics, covering robot kinematics, path planning, autonomous navigation, robot learning, and managing robotics.

AI Ethics and Governance

Covers AI ethics, covering bias and fairness, transparency, accountability, AI governance, responsible AI, and managing AI ethics.

Duration: 2 years
Public, up to: Ksh 254,100
Private, up to: Ksh 195,200
Job market: Very high

The programme

What you study, how long it takes, and how it is delivered.

Practicalities

Study mode: Full-time, Part-time, Blended
Attachment: 0 months
Average class: 20 students
Award: Masters

What you study

10 subjects

Machine Learning and Model Development
Deep Learning and Neural Networks
Natural Language Processing and Text Analytics
Computer Vision and Image Processing
Robotics and Autonomous Systems
AI Ethics and Responsible AI
Data Science and Big Data Analytics
Intelligent Systems and Knowledge Representation
Research Methods in AI
Thesis Research

Modules

12 in the programme

Foundations of Artificial Intelligence
Year 1Semester 13 creditsCore
Examines AI history, intelligent agents, search algorithms, knowledge representation, reasoning, planning, and managing AI foundations.
Machine Learning and Model Development
Year 1Semester 13 creditsCore
Covers supervised learning, unsupervised learning, reinforcement learning, model evaluation, feature engineering, and managing machine learning.
Research Methods in AI
Year 1Semester 13 creditsCore
Covers research design, data collection, analysis, ethical issues, and conducting AI research, preparing students for their thesis.
Deep Learning and Neural Networks
Year 1Semester 23 creditsCore
Examines neural networks, CNNs, RNNs, transformers, transfer learning, gradient descent, regularisation, and managing deep learning.
Natural Language Processing and Text Analytics
Year 1Semester 23 creditsCore
Covers text processing, language models, sentiment analysis, machine translation, chatbots, embeddings, and managing NLP.
Computer Vision and Image Processing
Year 1Semester 23 creditsCore
Examines image processing, object detection, facial recognition, scene understanding, segmentation, and managing computer vision.
Robotics and Autonomous Systems
Year 2Semester 13 creditsCore
Covers robot kinematics, path planning, autonomous navigation, robot learning, sensor fusion, and managing robotics.
AI Ethics and Responsible AI
Year 2Semester 13 creditsCore
Examines bias and fairness, transparency, accountability, AI governance, responsible AI, privacy, safety, and managing AI ethics.
Data Science and Big Data Analytics
Year 2Semester 13 creditsCore
Covers data preprocessing, big data analytics, data mining, statistical analysis, visualisation, and managing data science.
Intelligent Systems and Knowledge Representation
Year 2Semester 13 creditsCore
Examines knowledge representation, reasoning, expert systems, planning, decision-making, ontologies, and managing intelligent systems.
AI Application Development and Deployment
Year 2Semester 13 creditsCore
Covers AI application architecture, model deployment, MLOps, cloud AI services, API development, and managing AI applications.
Research Thesis or Project
Year 2Semester 26 creditsCore
Original research thesis or project on an AI topic, demonstrating mastery of research methods and AI knowledge, assessed through written submission and oral defence.

Specialisations

Machine Learning
Focuses on machine learning, covering supervised learning, unsupervised learning, reinforcement learning, model evaluation, and managing machine learning.
Deep Learning
Examines deep learning, covering neural networks, CNNs, RNNs, transformers, transfer learning, and managing deep learning.
Natural Language Processing
Covers NLP, covering text processing, language models, sentiment analysis, machine translation, chatbots, and managing NLP.
Computer Vision
Focuses on computer vision, covering image processing, object detection, facial recognition, scene understanding, and managing computer vision.
Robotics and Autonomous Systems
Examines robotics, covering robot kinematics, path planning, autonomous navigation, robot learning, and managing robotics.
AI Ethics and Governance
Covers AI ethics, covering bias and fairness, transparency, accountability, AI governance, responsible AI, and managing AI ethics.

A day as a student

A typical day during the MSc in Artificial Intelligence programme combines lectures, laboratory sessions, practical workshops, seminars, and independent study. Sessions cover machine learning, deep learning, NLP, computer vision, and AI ethics. Machine learning sessions examine supervised learning, unsupervised learning, reinforcement learning, model evaluation, feature engineering, and managing machine learning. Deep learning sessions cover neural networks, CNNs, RNNs, transformers, transfer learning, gradient descent, and managing deep learning. NLP sessions cover text processing, language models, sentiment analysis, machine translation, chatbots, embeddings, and managing NLP. Computer vision sessions cover image processing, object detection, facial recognition, scene understanding, segmentation, and managing computer vision. Robotics sessions cover robot kinematics, path planning, autonomous navigation, robot learning, sensor fusion, and managing robotics. AI ethics sessions cover bias and fairness, transparency, accountability, AI governance, responsible AI, privacy, and managing AI ethics. Data science sessions cover data preprocessing, big data analytics, data mining, statistical analysis, visualisation, and managing data science. Intelligent systems sessions cover knowledge representation, reasoning, expert systems, planning, decision-making, and managing intelligent systems. Research methods sessions prepare students for their thesis, covering research design, data collection, and analysis. Laboratory sessions provide hands-on experience with Python, TensorFlow, PyTorch, model training, deployment, and AI application development. Seminars and discussion groups provide opportunities for debating current trends in AI. Guest lectures from experienced AI engineers, data scientists, and industry leaders provide practical insights. The programme culminates in a research thesis or project on an AI topic.

The trade offs

In its favour

Very high demand for AI professionals with growing AI adoption across healthcare, agriculture, finance, governance, and technology sectors in Kenya.
Programme is offered by public (JKUAT, OUK) and private (Kabarak) universities, providing institutional and sectoral choice.
OUK offers very competitive fees at approximately KES 93,750/year (KES 187,500 total).
Programme covers cutting-edge areas including deep learning, NLP, computer vision, robotics, and AI ethics, providing versatile skills.

Against it

JKUAT fees are relatively high at approximately KES 254,100/year (KES 508,200 total).
Rapidly evolving field requires continuous self-learning beyond the programme curriculum.
Programme requires computer science, IT, or mathematics background, which may limit access for non-related graduates.
Limited number of universities offering the programme, though more are expected to launch AI programmes.

What it costs

Tuition at both ends of the market, and how to pay for it.

What it costs, and where

Against 191 technology courses

Public94k to 254k

94k at Open University of Kenya254k at Jomo Kenyatta University of Agriculture and Technology

Private195k to 195k

195k at Kabarak University195k at Kabarak University

Annual tuition in Kenyan shillings, rounded. The upright tick is the median for this field, so a bar sitting entirely to its right is an expensive programme by the standards of its own subject.

The fine print

OUK ~94K/yr (ouk.ac.ke). JKUAT ~254K/yr (jkuat.ac.ke). Kabarak MSc AI ~195K/yr (kabarak.ac.ke).

HELB postgraduate loans are available for Kenyan students. JKUAT may offer postgraduate bursaries for eligible students. Kabarak University may provide financial aid for eligible students. Some technology companies may sponsor staff for postgraduate AI study.

Funding options

HELB Postgraduate Loan
JKUAT Postgraduate Bursary
Kabarak Financial Aid

Scholarships

3 recorded

HELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate studies at recognised universities.
JKUAT Postgraduate Bursary
ScholarshipKsh 100,000Kenyan
JKUAT offers postgraduate bursaries for eligible students.
Kabarak Financial Aid
ScholarshipKsh 100,000Kenyan
Kabarak University provides financial aid for eligible students based on need and academic merit.

Getting in

The grades, the alternatives, and who accredits the award.

What you need

KCSE mean grade: N/A (Postgraduate)
Alternative entry: Most universities require Upper Second Class Honours in computer science, IT, data science, or mathematics. Lower Second Division holders with relevant experience or postgraduate diploma are considered. Pass degree holders with at least five years of experience may be considered. Contact respective universities for specific admission requirements.

How you are assessed

4 components

Coursework and Continuous Assessment
Coursework30% of the mark
Continuous assessment through coursework assignments, programming projects, laboratory reports, seminar presentations, and class participation.
Written Examinations
Examination40% of the mark
Written examinations covering machine learning, deep learning, NLP, computer vision, and AI ethics.
Laboratory and Project Assessment
Practical30% of the mark
Practical assessment through laboratory exercises, model development, AI application projects, deployment, and demonstrating AI skills.
Research Thesis or Project
Research100% of the mark
Original research thesis or project on an AI topic, demonstrating mastery of research methods and AI knowledge, assessed through written submission and oral defence.

Accreditation

The programme is accredited by the Commission for University Education (CUE). JKUAT and Open University of Kenya (public) and Kabarak University (private) offer MSc in Artificial Intelligence. All programmes meet CUE standards for postgraduate training in AI. Graduates are eligible for Google AI certification and AAAI AI professional certification.

Accredited by

Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. JKUAT and Open University of Kenya (public) and Kabarak University (private) offer MSc in Artificial Intelligence. All programmes meet CUE standards for postgraduate training in AI. Graduates are eligible for Google AI certification and AAAI AI professional certification.

Where it leads

The roles it opens, and what you leave with.

Where graduates go

6 roles

AI Engineer
Very high demandKsh 180,000 to Ksh 700,000
Develops AI systems, overseeing model design, training, deployment, evaluation, and managing AI engineering.
Machine Learning Engineer
Very high demandKsh 170,000 to Ksh 650,000
Develops ML models, overseeing model development, training, optimisation, deployment, and managing ML engineering.
Data Scientist
Very high demandKsh 160,000 to Ksh 600,000
Analyses data using AI/ML, overseeing data analysis, modelling, visualisation, insight generation, and managing data science.
NLP Engineer
High demandKsh 170,000 to Ksh 650,000
Develops NLP systems, overseeing text processing, language models, chatbots, translation, and managing NLP engineering.
Computer Vision Engineer
High demandKsh 170,000 to Ksh 650,000
Develops computer vision systems, overseeing image processing, object detection, recognition, and managing computer vision engineering.
AI Lecturer
Moderate demandKsh 130,000 to Ksh 500,000
Teaches AI at university or college level, overseeing instruction, research, laboratory supervision, and academic supervision.

Graduate outcomes

Where these fields lead

8 careers

Tools you will learn

Python
SoftwarePrimary
Python for AI development, covering numpy, pandas, scikit-learn, data manipulation, model development, and managing AI projects.
TensorFlow
Software
TensorFlow for deep learning, covering neural networks, model training, deployment, transfer learning, and managing deep learning.
PyTorch
Software
PyTorch for deep learning, covering neural networks, dynamic computation, model training, research, and managing deep learning.
Hugging Face
Platform
Hugging Face for NLP and transformers, covering pre-trained models, fine-tuning, model deployment, and managing NLP models.

Industry links

Common misconceptions

AI is just about chatbots.
AI covers comprehensive machine learning, deep learning, NLP, computer vision, robotics, and intelligent systems beyond just chatbots.
This programme is only for computer scientists.
AI skills are valuable for anyone with mathematics, statistics, engineering, or data background interested in intelligent systems and data-driven innovation.
AI will replace human jobs entirely.
AI augments human capabilities, creating new roles in AI development, ethics, governance, and human-AI collaboration rather than wholesale replacement.
Deep learning is just about bigger neural networks.
Deep learning covers comprehensive architecture design, optimisation, regularisation, transfer learning, and domain-specific model development.
AI ethics is just about avoiding bias.
AI ethics covers comprehensive fairness, transparency, accountability, privacy, governance, safety, and responsible AI deployment.
Computer vision is just about image classification.
Computer vision covers comprehensive image processing, object detection, segmentation, scene understanding, 3D vision, and video analysis.