Master of Science in Applied Machine Learning
There is a large and growing population of technically capable professionals in the region who can program and reason quantitatively but who cannot yet build, evaluate and deploy machine learning systems to a professional standard. They do not need a research degree; they need a rigorous, build ...
The four facts
What you actually need to compare
- Award and entry
- MSc
- A
- Duration and mode
- 18 months (3 semesters)
- Hybrid. Includes 0 months of paid, assessed cooperative education.
- Cost
- UGX 3,300,000 per year
- Tier 1 laboratory classification. Includes UGX 300,000 annual laboratory premium.
- Outcome targeted
- Machine learning engineer, applied scientist, data scientist
- Reviewed by 2031. If the thesis stops holding, the programme is reviewed.
Pricing Model 5.1 · The Forward Standard
Master of Science in Applied Machine Learning fee
UGX 3,300,000 per year
- Laboratory tier
- Tier 1
- Annual premium
- UGX 300,000
- Per semester
- UGX 1,650,000
- Whole programme
- UGX 4,950,000
- Approximate USD
- ≈ USD 1,303
What this fee includes
- Teaching, practice tutoring, assessment and competency verification
- The Amagezi platform and AI tutor around the clock
- All learning materials
- Every examination, including first resits
- Identity card, library, guild, sports and basic wellbeing
- Co-op placement matching and the Capability Passport
- Graduation and the statutory NCHE fee
- Online application
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
- data scientist
- MLOps engineer
- AI product engineer
- model evaluation specialist
- analytics lead
- technical founder in an applied domain
Employer types
- Banks
- insurers and mobile money operators
- health and agricultural technology firms
- telecommunications operators
- government digital and statistical agencies
- regional and global technology firms
- international research and development organisations
- consulting firms
- and the graduate's own venture
- incubated through the Forward Venture Studio
Target outcomes we hold ourselves to
- This programme is built for working professionals and recent quantitative graduates converting into applied machine learning roles, and is explicitly designed to support remote employment at international rates. Progression routes include the MSc in Machine Learning and Foundation Models and doctoral study for those who move toward research.
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
Postgraduate programmes carry work integrated learning inside the taught structure rather than the twelve month undergraduate cooperative education placement. Read the co-op commitment
Am I eligible?
Check yourself against the published requirements
7 questions written specifically for Master of Science in Applied Machine Learning. 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?
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 501 | Applied Intelligence for Professionals Both | 4 | 2-2-0 |
| FDC 502 | Research Evidence and Method Both | 4 | 2-2-0 |
| MAML 510 | Machine Learning Foundations and Practice Both | 6 | 3-2-2 |
| MAML 511 | Data Engineering for Machine Learning Performance | 6 | 3-2-4 |
| MAML 512 | Deep Learning and Representation Both | 5 | 3-2-2 |
| MAML 513 | Evaluation Science and Honest Benchmarking Both | 5 | 3-2-2 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 503 | Governance, Ethics and Leadership of Intelligent Systems Both | 4 | 2-2-0 |
| MAML 520 | Applied Foundation Models and Adaptation Performance | 7 | 3-2-4 |
| MAML 521 | Machine Learning Systems Engineering and MLOps Both | 5 | 3-2-2 |
| MAML 522 | Uncertainty, Risk and Responsible Deployment Performance | 5 | 2-2-4 |
| MAML 523 | Applied Domain Elective Performance | 5 | 2-2-4 |
| MAML 524 | Advanced Applied Elective Objective | 4 | 3-2-0 |
| Code | Course | CU | Hours |
|---|---|---|---|
| MAML 530 | Machine Learning Deployment Project and Dissertation Performance | 20 | 0-2-12 |
| MAML 531 | Doctoral and Research Preparation Seminar Objective | 5 | 3-2-0 |
| MAML 532 | Research Translation and Commercialisation Objective | 5 | 3-2-0 |
Total credit units: 90
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 Master of Science in Applied Machine Learning
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
SEMESTER 1 | YEAR 1 | 30 COMPETENCY UNITS
Year 1 · this step is worth 30 CU
Cumulative
30
Programme total
90
30 CU of 90 CU completed by the end of this step (33%)
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 Reasoning CriticalLevel 4 of 4, Leading
- C3Ethical and Contextual JudgementLevel 3 of 4, Independent
- C4Communication and PersuasionLevel 3 of 4, Independent
- C5Disciplinary MasteryLevel 4 of 4, Leading
- C6Technical Production and CraftLevel 4 of 4, Leading
- C7Problem Framing and Systems ThinkingLevel 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 · Quantitative and Evidential Reasoning. 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 a real world problem as a machine learning problem, select an appropriate method, and justify it against alternatives and against not using machine learning at all.
- PLO 2Build, train and evaluate models across the supervised, unsupervised and sequence paradigms using an evaluation harness the student designed.
- PLO 3Engineer the data substrate for a machine learning system: acquisition, quality, labelling, bias measurement and versioning.
- PLO 4Deploy a model into production and operate it: serving, monitoring, drift detection, cost control and controlled retirement.
- PLO 5Quantify and communicate uncertainty and identify the conditions under which a deployed system will fail.
- PLO 6Apply and adapt a pre- trained foundation model to a domain task under real data and compute constraints.
- PLO 7Evaluate a machine learning system honestly, resisting the leaderboard and exposing contamination and leakage.
- PLO 8Translate between a model and the people whose decisions it affects, and defend a deployment decision to them.
- PLO 9Deliver an applied machine learning project for an external host with a measured outcome.
- PLO 10Acquire and demonstrate a new technical capability without formal instruction, evidenced in the Capability Passport.
Why this programme exists
There is a large and growing population of technically capable professionals in the region who can program and reason quantitatively but who cannot yet build, evaluate and deploy machine learning systems to a professional standard. They do not need a research degree; they need a rigorous, build heavy conversion into applied machine learning that ends in deployed systems rather than in a dissertation. This programme is that conversion. It is deliberately distinct from the research intensive MSc in Machine Learning and Foundation Models: the emphasis here is on applying well understood methods to real problems, engineering the data and the deployment, and evaluating honestly, for the practitioner who will carry machine learning into a bank, a hospital, a farm or a ministry.
Admission requirements
A good first degree (second class or above) in a quantitative or computing discipline, or a first degree in another field with demonstrated programming and quantitative competence; plus a short technical task at admission scored against Capabilities C2 and C6. Substantial professional experience in a data or engineering role is admitted through recognition of prior learning against the same profile. All applicants must demonstrate competence in English, the language of instruction, through their qualifications or an approved proficiency test under Part A. International and non standard qualifications are assessed for equivalence against the profile above under the Credential Portability provisions, so that a suitably prepared applicant may enter from anywhere in the world. Selection considers the whole application, including the structured admission task, and is conducted without discrimination under the University's admissions policy. Alternative pathways: recognition of prior learning against the admission profile under Part G, including substantial professional practice in lieu of the named first degree field. Online components require the Digital Readiness orientation under Part D6.