Put up to three degrees next to each other
School and award, credit totals, the term and block paths, paid co-op, the programme learning outcomes and the twelve-capability radar, all read directly from the Curriculum Compendium, with no marketing in between. Share the link and the comparison comes with it.
Capability profile
The twelve capabilities, overlaid
Each programme is plotted on the four-level scale in Part B2 of the Curriculum Compendium: Aware, Capable, Proficient, Leading. A missing point means the programme does not map that capability.
- C1Intelligent Systems Fluency44
- C2Quantitative and Evidential Reasoning44
- C3Ethical and Contextual Judgement33
- C4Communication and Persuasion34Differs
- C5Disciplinary Mastery44
- C6Technical Production and Craft43Differs
- C7Problem Framing and Systems Thinking44
- C8Collaboration and Multidisciplinary Teaming33
- C9Enterprise, Value and Commercial Literacy34Differs
- C10Professional Conduct and Workplace Performance33
- C11Learning to Learn and Adaptive Capacity43Differs
- C12Stewardship and Public Contribution33
Scroll the table sideways on a narrow screen, the attribute column stays put.
BSc (Hons)
School of Artificial Intelligence and Computing
BSc (Hons)
School of Artificial Intelligence and Computing
At a glance
Where each programme sits in the academic architecture, and what it commits to at entry.
School
Same- School of Artificial Intelligence and Computing
- School of Artificial Intelligence and Computing
Department
Differs- Department of Machine Learning and Foundation Models
- Department of Data Science and Analytics
Credential
Same- BSc (Hons)
- BSc (Hons)
Programme code
Differs- FU/AIC/BSAI
- FU/AIC/BDSA
Delivery modes
Published availability, not preference.
Same- Campus
- Online
- Hybrid
- Campus
- Online
- Hybrid
Duration
Differs- 4 years (8 semesters)
- 3 years (6 semesters)
Credit units
Differs- 176 CU
- 132 CU
Paid co-op
Work-integrated learning is compulsory on every Forward programme.
Same- 12 months
- 12 months
Next intake
Same- August 2027
- August 2027
Accreditation status
Same- First accreditation wave
- First accreditation wave
Term model (semesters)
The conventional path: six-month terms, courses running in parallel.
Terms to completion
Differs- 8 terms
- 6 terms
Courses in the term path
Differs- 39 courses
- 29 courses
Credit units per term (average)
Total published credit units divided by the number of terms.
Same- 22 CU
- 22 CU
Term sequence
Differs- Year 1, 22 CU
- Year 1, 22 CU
- Year 2, 22 CU
- Year 2, 22 CU
- Year 3, 22 CU
- Year 3, 22 CU
- Year 4, 22 CU
- Year 4, 22 CU
- Year 1, 22 CU
- Year 1, 22 CU
- Year 2, 22 CU
- Year 2, 22 CU
- Year 3, 22 CU
- Year 3, 22 CU
Block model (intensive modules)
The same degree mapped as sequential blocks: one module at a time, assessed at the end of each.
Blocks to completion
Differs- 36 blocks
- 25 blocks
Taught weeks in the block path
Differs- 188 weeks
- 144 weeks
Stages the blocks are grouped into
Differs- 4 stages
- 3 stages
Credit units in the block path
Both paths carry the same award, so the totals should match the term path.
Differs- 168 CU
- 122 CU
Programme learning outcomes
What a graduate must be able to do, and the Forward capabilities each outcome is verified against.
Outcomes published
Programme learning outcomes as published in the Curriculum Compendium.
Differs- 10 outcomes
- 9 outcomes
PLO 1
Differs- Formulate 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.
- Verified against C1, C7
- Acquire, clean, join, document and govern a real administrative or commercial dataset and state honestly what it can and cannot support.
- Verified against C2, C5
PLO 2
Differs- Build, train, fine tune and evaluate models across supervised, self-supervised, sequence, vision and reinforcement paradigms, using an evaluation harness the student designed.
- Verified against C1, C5, C6
- Design and execute an appropriate statistical analysis including experimental and quasi experimental designs and quantify uncertainty correctly.
- Verified against C2
PLO 3
Differs- Engineer the data substrate for a machine learning system: acquisition, licensing, labelling, quality, bias measurement versioning and governance.
- Verified against C1, C2, C3
- Distinguish correlation from causation in a real policy or commercial question and apply an appropriate causal identification strategy.
- Verified against C2, C7
PLO 4
Differs- Deploy a model into production and operate it: serving monitoring, drift detection, incident response, cost control and controlled retirement.
- Verified against C6, C10
- Build, evaluate and deploy predictive and forecasting models, and monitor them in operation.
- Verified against C1, C6
PLO 5
Differs- Quantify and communicate uncertainty, and identify the specific conditions under which a deployed system will fail.
- Verified against C2, C4
- Design visualisations and dashboards that change what a decision maker does, and defend them under challenge.
- Verified against C4
PLO 6
Differs- Red team an intelligent system, document its failure modes, and write the assurance report a regulator or board would act on.
- Verified against C1, C3, C12
- Write and defend a decision memorandum that converts analysis into a recommendation with costed consequences.
- Verified against C4, C9
PLO 7
Differs- Build a language, speech or vision model for an African language or context under genuine data and compute constraints.
- Verified against C6, C12
- Identify measurement bias, sampling bias and algorithmic harm in a dataset or model, and specify the remedy.
- Verified against C3
PLO 8
Differs- Work as the technical member of a multidisciplinary team translating between the model and the people whose decisions it affects.
- Verified against C4, C8
- Use intelligent tools to accelerate analysis while detecting and correcting the errors they introduce.
- Verified against C1, C11
PLO 9
Differs- Cost, pitch and defend an AI product or venture proposal to an audience including an investor and an affected user.
- Verified against C9
- Deliver an analytical product for a real external client against a real deadline.
- Verified against C10, C12
PLO 10
Differs- Acquire and demonstrate a new technical capability in this fast moving field without formal instruction, evidenced in the Capability Passport.
- Verified against C11
- Not yet published
Employment thesis
What each programme claims it produces, and when that claim is next tested.
Roles the programme targets
Differs- 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.
- Data scientist
- analytics engineer
- business intelligence lead
- decision scientist
- statistician
- monitoring and evaluation analyst
- credit and risk analyst
- growth and product analyst
- public sector data analyst
- survey and impact analyst.
Employer types
Differs- 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.
- Banks, insurers and mobile money operators
- telecommunications operators
- retail and FMCG
- development agencies and impact funds
- ministries and statistical agencies
- health systems
- agricultural value chain firms
- and consulting practices.
Outcomes Forward holds itself to
Differs- 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.
- Positioned for immediate formal sector employment in Uganda and the region, with strong remote work demand internationally. Progression to the MSc in Applied Artificial Intelligence, the MSc in Data Engineering and Cloud Architecture, or professional actuarial and analytics certifications.
Thesis next reviewed
Same- 2031
- 2031
Assessment regime
Same- Not yet published
- Not yet published
Work-integrated learning
Same- Not yet published
- Not yet published
Getting in
Published admission requirements, side by side.
Admission requirements
Differs- 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.
- UACE with two principal passes including Mathematics or Economics, plus UCE with five passes including Mathematics and English. A quantitative reasoning task is sat at admission. 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.
Why the programme exists
Differs- 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.
- Every institution in Uganda now holds more data than it can interpret, and almost none of them can convert it into a decision. The gap is not modelling talent; it is the far scarcer ability to move from a messy administrative dataset to a defensible recommendation that a board, a ministry or a cooperative will act on. This programme is therefore built around decision quality rather than technique. Statistics and causal inference are taught early and heavily, because the most expensive analytical failures in the region are causal ones. Every course uses genuine Ugandan and African data — census, agricultural, clinical, mobile money, market and administrative — and every major assessment ends with a decision memorandum defended to a non-technical audience.
Capability profile
The twelve Forward capabilities, on the Part B2 scale: Aware, Capable, Proficient, Leading. A star marks a capability flagged Critical for that programme.
C1 · Intelligent Systems Fluency
C2 · Quantitative and Evidential Reasoning
C3 · Ethical and Contextual Judgement
C4 · Communication and Persuasion
1 level apartC5 · Disciplinary Mastery
C6 · Technical Production and Craft
1 level apartC7 · Problem Framing and Systems Thinking
C8 · Collaboration and Multidisciplinary Teaming
C9 · Enterprise, Value and Commercial Literacy
1 level apartC10 · Professional Conduct and Workplace Performance
C11 · Learning to Learn and Adaptive Capacity
1 level apartC12 · Stewardship and Public Contribution
Both study paths lead to the same award. Where a figure is missing, the compendium has not published it yet rather than it being zero.