Bachelor of Science in Artificial Intelligence
Artificial intelligence and big data is the fastest growing skill category in the world, two thirds of employers intend to hire specifically for it, and the measured wage premium for demonstrable AI capability now exceeds sixty per cent of ...
Employment thesis
What this programme is designed to produce
Forward publishes the destination of every programme before you enrol. If the thesis stops holding, the programme is reviewed and, where necessary, retired. Next review: 2031.
Roles
- Machine learning engineer
- applied scientist
- AI engineer
- computer vision engineer
- NLP and speech engineer
- MLOps engineer
- model evaluation and red team specialist
- AI solutions architect
- research engineer
- technical founder.
Employer types
- Frontier and applied AI laboratories
- regional and global technology firms
- banks, insurers and mobile money operators
- telecommunications operators
- health and agricultural technology firms
- government digital and statistical agencies
- international research institutes
- and the graduate's own venture, incubated through the Forward Venture Studio.
Target outcomes we hold ourselves to
- This programme targets the upper decile of graduate technology compensation in East Africa and is explicitly designed for remote employment by international employers at international rates. Progression routes include the MSc in Machine Learning and Foundation Models and doctoral study.
Compulsory · The Forward Core
How the Core works →Every Forward programme, including this one, carries the same five cross-cutting requirements. They are graded, not optional.
- AI Fluency
- Quantitative Reasoning
- Ethics and African Context
- Human-Advantage Skills
- Enterprise
Includes 12 months paid work-integrated learning
Am I eligible?
Check yourself against the published requirements
7 questions written specifically for Bachelor of Science in Artificial Intelligence. Every answer is checked against a requirement Forward has published, and the result tells you which page that requirement comes from. Indicative only, the admissions office decides on your full file.
What is the highest qualification you hold or are completing?
Curriculum structure
The same degree, in both pathways
Every Forward programme is published in both pathways: Path One, the term plan of two six-month terms, and Path Two, the block sequence of twelve four-week blocks. Same competencies, same assessments, same award. Toggle to see this programme in each.
Path One term plan shown
Path One · The Term Model
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 101 | AI Fluency I: How Intelligent Systems Work Both | 4 | 2-2-0 |
| AIC 110 | Programming and Computational Thinking I Performance | 5 | 2-2-4 |
| AIC 111 | Discrete Mathematics for Computing Objective | 4 | 3-2-0 |
| AIC 112 | Linear Algebra for Machine Learning Both | 5 | 3-2-2 |
| AIC 113 | Foundations of Data and Databases Both | 4 | 2-2-2 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 102 | Quantitative Reasoning and Evidence Both | 4 | 2-2-0 |
| AIC 130 | Programming and Computational Thinking II: Data Structures and Algorithms Performance | 5 | 2-2-4 |
| AIC 131 | Probability and Statistical Inference Both | 5 | 3-2-2 |
| AIC 132 | Calculus and Optimisation Foundations Objective | 4 | 3-2-0 |
| AIC 133 | Computer Systems, Architecture and Operating Systems Both | 4 | 2-2-2 |
| COP 190 | Workplace Immersion (4 weeks, recess term) Performance | 0 | 0-0-0 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 201 | Ethics, Society and the African Context Both | 4 | 2-2-0 |
| AIC 210 | Introduction to Machine Learning Performance | 5 | 2-2-4 |
| AIC 211 | Data Engineering and Pipelines Both | 4 | 2-2-2 |
| AIC 212 | Software Craft: Version Control, Testing and Review Performance | 4 | 1-0-6 |
| AIC 213 | Numerical Methods and Scientific Computing Both | 5 | 3-2-2 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 202 | Human Advantage: Argument, Writing and Teams Both | 4 | 2-2-0 |
| COP 290 | Co-operative Education Placement I (6 months) Performance | 10 | 0-0-0 |
| AIC 230 | Deep Learning I: Neural Networks and Training Performance | 6 | 3-2-4 |
| AIC 231 | Databases at Scale and Distributed Data Both | 2 | 1-0-2 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 301 | AI Fluency II: Building, Evaluating and Auditing Both | 4 | 2-2-0 |
| AIC 310 | Deep Learning II: Sequence Models, Transformers and Attention Performance | 6 | 3-2-4 |
| AIC 311 | Computer Vision and Visual Perception Performance | 5 | 2-2-4 |
| AIC 312 | Natural Language Processing and Speech Performance | 5 | 2-2-4 |
| AIC 313 | Machine Learning Systems Engineering and MLOps Performance | 2 | 1-0-2 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 302 | Enterprise, Employability and Financial Literacy Both | 4 | 2-2-0 |
| COP 390 | Co-operative Education Placement II (6 months) Performance | 10 | 0-0-0 |
| AIC 330 | Reinforcement Learning and Sequential Decision Making Performance | 5 | 2-2-4 |
| AIC 331 | Model Evaluation Science Both | 3 | 1-2-2 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 401 | AI Fluency III: Domain Deployment, Governance and Assurance Both | 3 | 1-2-0 |
| AIC 410 | Foundation Models: Pre-training, Fine tuning and Alignment Performance | 6 | 3-2-4 |
| AIC 411 | African Language and Low Resource Modelling Performance | 5 | 2-2-4 |
| AIC 412 | AI Safety, Red Teaming and Assurance Both | 4 | 2-2-2 |
| AIC 413 | Specialisation Elective I Objective | 4 | 3-2-0 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 402 | Capability Portfolio and Day One Preparation Both | 3 | 1-2-0 |
| AIC 430 | AI Capstone: Real Client Intelligent System (client verified) Performance | 9 | 0-2-16 |
| AIC 431 | Efficient AI: Quantisation, Distillation and Constrained Compute Both | 4 | 2-2-2 |
| AIC 432 | Specialisation Elective II Objective | 3 | 2-2-0 |
| AIC 433 | Professional Practice and Technical Leadership Objective | 3 | 2-2-0 |
Total credit units: 176
Curriculum source
Forward University Curriculum Compendium
Assessment
Coursework + project + integrated exam
Work-integrated learning
12 months paid co-operative education
Your week at Forward
A typical week on Bachelor of Science in Artificial Intelligence
Path One, the Term Model: your own pace across a six-month term, one live session a week, short flexible lessons, and assessment on demand.
Monday
Tuesday
Wednesday
Thursday
Friday
Curriculum explorer
Drill into the degree, step by step
Choose a study path, then a year, then a term or block, then a course. Credit totals add up as you go, and every course shows what comes before it and what it unlocks.
Step 1, choose a year
Step 2, choose a term
TERM 1 (SIX MONTHS) | FORMERLY YEAR 1, SEMESTER 1
Year 1 · this step is worth 22 CU
Cumulative
22
Programme total
176
22 CU of 176 CU completed by the end of this step (13%)
Step 3, choose a course
Pick a course to see the detail
Credits, assessment, prerequisites and what each course unlocks later.
The twelve Forward capabilities
What this programme develops in you
Every Forward degree develops the same twelve cross-cutting capabilities. The profile below is the level this programme is designed to develop and verify by graduation, on the four-level scale in Part B2 of the Curriculum Compendium. Every claim above Level 2 is verified by someone other than the teaching lecturer.
- C1Intelligent Systems Fluency CriticalLevel 4 of 4, Leading
- C2Quantitative and Evidential ReasoningLevel 4 of 4, Leading
- C3Ethical and Contextual JudgementLevel 3 of 4, Independent
- C4Communication and PersuasionLevel 3 of 4, Independent
- C5Disciplinary Mastery CriticalLevel 4 of 4, Leading
- C6Technical Production and Craft CriticalLevel 4 of 4, Leading
- C7Problem Framing and Systems Thinking CriticalLevel 4 of 4, Leading
- C8Collaboration and Multidisciplinary TeamingLevel 3 of 4, Independent
- C9Enterprise, Value and Commercial LiteracyLevel 3 of 4, Independent
- C10Professional Conduct and Workplace PerformanceLevel 3 of 4, Independent
- C11Learning to Learn and Adaptive CapacityLevel 4 of 4, Leading
- C12Stewardship and Public ContributionLevel 3 of 4, Independent
Critical capabilities for this programme: Intelligent Systems Fluency · Disciplinary Mastery · Technical Production and Craft · Problem Framing and Systems Thinking. A graduate cannot pass out of this programme below the stated level on any capability marked critical.
Programme Learning Outcomes
What you will be able to do
Each outcome is assessed, and each is tagged with the Forward capabilities it is verified against.
- PLO 1Formulate an ill defined real world problem as a learning problem, select an appropriate model class, and justify the choice against alternatives and against not using machine learning at all.
- PLO 2Build, train, fine tune and evaluate models across supervised, self-supervised, sequence, vision and reinforcement paradigms, using an evaluation harness the student designed.
- PLO 3Engineer the data substrate for a machine learning system: acquisition, licensing, labelling, quality, bias measurement versioning and governance.
- PLO 4Deploy a model into production and operate it: serving monitoring, drift detection, incident response, cost control and controlled retirement.
- PLO 5Quantify and communicate uncertainty, and identify the specific conditions under which a deployed system will fail.
- PLO 6Red team an intelligent system, document its failure modes, and write the assurance report a regulator or board would act on.
- PLO 7Build a language, speech or vision model for an African language or context under genuine data and compute constraints.
- PLO 8Work as the technical member of a multidisciplinary team translating between the model and the people whose decisions it affects.
- PLO 9Cost, pitch and defend an AI product or venture proposal to an audience including an investor and an affected user.
- PLO 10Acquire and demonstrate a new technical capability in this fast moving field without formal instruction, evidenced in the Capability Passport.
Why this programme exists
Artificial intelligence and big data is the fastest growing skill category in the world, two thirds of employers intend to hire specifically for it, and the measured wage premium for demonstrable AI capability now exceeds sixty per cent of comparable salaries. Africa's constraint is not demand and no longer, after the current wave of data centre investment, compute. It is trained people. Yet the programmes offered across the region remain overwhelmingly conversions of computer science degrees with two machine learning courses added in the final year, taught without access to accelerators and assessed by written examination. This programme is built the other way round. Machine learning is the spine from Year 1; computer science is taught as the foundation that machine learning requires rather than as an end in itself; and every year of study terminates in a working system that somebody outside the University has used. The African language and low resource specialisation exists because the models the continent most needs are the ones nobody else has a commercial reason to build.
Admission requirements
Uganda Advanced Certificate of Education with two principal passes including Mathematics, and at least a subsidiary pass in Physics, Economics, Computer Studies or another quantitative subject; plus Uganda Certificate of Education with five passes including Mathematics and English. Applicants additionally sit a structured quantitative and problem framing task at admission, scored against Capabilities C2 and C7. Equivalent international qualifications are assessed against this profile. Alternative pathways: Foundation Year, diploma entry with advanced standing, mature age entry and recognition of prior learning, all governed by Part A5 and Part G. Online applicants additionally complete the compulsory Digital Readiness orientation under Part D6.
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