Our Programmes
Three courses. A single, considered direction.
Each programme is designed for a specific stage of AI learning — from statistical foundations through to independent engagement with the research literature.
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How our programmes are structured
Each Mongkut AI programme follows the same underlying philosophy: introduce a concept carefully, build understanding through worked examples, then consolidate it through applied exercises. The order of topics within a programme is deliberate — earlier material provides the language and tools that later material requires.
Assignments are designed to test genuine understanding rather than recall. For the longer programmes, written feedback from the instructional team is part of the cycle: you submit work, receive a substantive written response, and revise your thinking where necessary before moving on.
The typical learner on our programmes is working professionally and can commit a few hours each week to study. Programmes are designed with that constraint in mind — the pace is sustainable, and there are no arbitrary external deadlines.
Sequential structure
Topics build on each other. No section assumes knowledge that hasn't been covered yet.
Written assignments
Assessments require written reasoning, not just multiple-choice selections.
Python throughout
Applied exercises in Python are embedded within each programme, not appended.
Instructor feedback
Written responses from the instructional team for substantive assignments.
Programme 1
Foundations of Statistics for AI
A foundational course covering the statistical concepts that underlie modern AI methods — probability, distributions, hypothesis testing, and the foundations of statistical inference. Material is presented with attention to building genuine understanding, supported by applied exercises in Python. Suitable for learners returning to statistics after some time away, or those whose AI study has highlighted gaps they would now like to address.
- Probability theory and distribution families
- Hypothesis testing and statistical inference
- Applied Python exercises with NumPy and SciPy
- Connections to ML methods made explicit
Course process
Pre-course assessment to determine the right starting point within the material
Structured lessons covering theory, followed by worked examples and Python exercises
End-of-module written assignment with optional instructor review
Completion acknowledgement and guidance on next steps in your AI learning
Duration: 8–10 weeks
฿2,600
Programme 2
Reinforcement Learning Programme
A programme focused on reinforcement learning — covering the foundational framework of Markov decision processes, the classical algorithms, and the contemporary methods that combine deep learning with RL in modern systems. The programme combines structured lessons with applied projects in established environments. Students work at a pace that allows for careful engagement with both the mathematical content and the implementation work, with regular written feedback from the instructional team.
- Markov decision processes and Bellman equations
- Classical algorithms: Q-learning, SARSA, and policy gradients
- Deep RL methods and contemporary approaches
- Applied projects with written instructor feedback
Course process
Foundational MDP framework established through structured lessons and exercises
Classical RL algorithms implemented in Python in standard gym environments
Deep RL section with project work and written feedback at each major submission
Final applied project demonstrating end-to-end understanding of an RL system
Duration: 3–4 months
฿6,400
Programme 3
Research Methods in AI
A programme for learners who want to develop the skills required to engage with the AI research literature directly — reading papers, understanding their methods and contributions, and developing the discipline to evaluate claims carefully. The programme combines structured readings with guided discussions and small written reviews. Suitable for learners who have completed foundational AI study and now wish to develop the research-oriented thinking that supports more advanced work in the field.
- Paper structure and how to navigate it efficiently
- Evaluating experimental design and claimed contributions
- Guided discussions on selected papers from the literature
- Written reviews with detailed instructor response
Course process
Introduction to paper structure, common conventions, and reading strategies
Guided reading of selected papers with structured discussion questions
Written reviews submitted fortnightly with instructor written response
Independent paper selection and review as a final assessment
Duration: 3–4 months
฿8,000
Which Programme?
Choosing the right starting point
If you are uncertain which programme to begin with, this table summarises the key differences. You are also welcome to contact us to discuss your background directly.
| Feature | Statistics for AI | Reinforcement Learning | Research Methods |
|---|---|---|---|
| Suitable for beginners | |||
| Python exercises included | Limited | ||
| Written instructor feedback | Optional | ||
| Focuses on research literature | |||
| Deep learning required prior | Helpful | ||
| Price (THB) | ฿2,600 | ฿6,400 | ฿8,000 |
| Best for… | Filling statistical gaps | Building RL capability | Reading the literature independently |
Standards
What applies across all programmes
Student privacy
Student records, correspondence, and assignment submissions are handled with care and not shared with third parties outside the school's operation.
Annual curriculum review
Course materials are reviewed each year against developments in the field. Outdated content is revised or replaced before the following cohort begins.
Responsive support
Content questions directed to the school by email are responded to within two to three working days by a member of the instructional team.
Secure platform access
Course materials and student accounts are accessible through a platform that is kept up to date. Login credentials are managed securely.
Clear syllabus before enrolment
Each programme comes with a detailed syllabus that covers topics, expected time commitment, prerequisites, and the assessment format — available before any payment is made.
Honest outcome descriptions
We describe what a course teaches and how it is assessed. We do not make claims about employment outcomes or use language that inflates what completing a programme will do.
Pricing
Programme fees
All fees are in Thai Baht (฿) and are paid in full at enrolment.
Programme 1
Foundations of Statistics for AI
One-time payment at enrolment
- Full course access
- All Python exercises and materials
- Email support from instructional team
- Completion acknowledgement
Programme 2
PopularReinforcement Learning Programme
One-time payment at enrolment
- Full programme access
- Applied Python projects with environments
- Written feedback on all major assignments
- Email support throughout
Programme 3
Research Methods in AI
One-time payment at enrolment
- Full programme access
- Curated reading list and discussion materials
- Written feedback on fortnightly reviews
- Independent final review with instructor response
Not sure where to start?
Write to us before you enrol
We are glad to discuss your background, answer questions about the content, and help you identify the right programme for where you are now.
Send an Enquiry