Three programmes. Three distinct levels of engagement with AI.
Each programme is designed for a specific kind of learner at a specific point in their development. Read the descriptions carefully — they are written to help you decide which one, if any, is right for where you are now.
Back to HomeHow the programmes are structured
Every programme at Lotus Studio begins with a clear statement of who it is for and what it assumes the learner already knows. That transparency is intentional — learners who start at the right level make considerably more progress than those who arrive underprepared.
Content is delivered asynchronously so learners can work at a pace that suits the difficulty of the material. Applied exercises are integrated throughout, not appended at the end. The exercises are chosen because they make the underlying concepts clearer, not to add volume to the course.
All three programmes are reviewed twice yearly. When the field develops in a way that changes what a working understanding requires, the curriculum is updated to reflect that.
Mathematics for AI
A course covering the mathematical foundations that underpin modern AI work, including linear algebra, calculus, optimisation, and the elements of probability and statistics relevant to the field. The material is presented carefully with attention to building genuine understanding, supported by applied exercises that connect the mathematics to its uses in machine learning. Suitable for learners returning to mathematics after some time away, or those whose AI study has highlighted foundational gaps they would now like to address before moving further.
- Vectors, matrices, and linear transformations — including the decompositions central to ML
- Calculus for optimisation: derivatives, gradients, and the chain rule as it applies to neural networks
- Probability fundamentals, distributions, and Bayes' theorem in the ML context
- Statistical estimation and hypothesis testing at the level needed for AI work
- Applied exercises connecting each mathematical topic to a machine learning application
- 1 Enrol and access all materials immediately
- 2 Work through modules at your own pace — each builds on the previous
- 3 Complete applied exercises at the end of each module
- 4 Submit questions to the instructor when difficulty arises
- 5 Complete at your own pace — typical duration 6 to 8 weeks
Generative AI & Diffusion Models
A programme focused on contemporary generative methods, including diffusion models for image generation, the relationship between diffusion and other generative approaches, and the practical considerations involved in working with these systems. The programme combines structured lessons drawing on the foundational papers with applied exercises using established frameworks. Suitable for learners with prior machine learning experience who would like to develop careful working knowledge of the generative side of the field.
- The theory of diffusion models — forward and reverse processes, score matching, and DDPM
- Relationship between diffusion, VAEs, flows, and other generative approaches
- Reading and analysing the key papers in the diffusion model literature
- Practical exercises implementing core components using established Python frameworks
- Conditioning, guidance, and applications across image and other domains
- 1 Enrol and receive access to all programme materials
- 2 Work through paper-grounded lessons in sequence
- 3 Complete applied coding exercises with framework tools
- 4 Submit questions to the instructor for guidance
- 5 Complete at your own pace — typical duration 10 weeks
AI Research Pathway
A long-form pathway for learners considering a research direction in AI, whether through graduate study or independent work. The pathway combines structured engagement with the research literature, the development of a small original research project under mentorship, and careful guidance on the practices of research communication. Suitable for learners who have completed substantial AI study and now wish to develop the research orientation and habits that distinguish those who contribute to the field from those who apply existing methods.
- Systematic reading of AI research literature — how to read, analyse, and situate papers
- Identifying a small, original research question appropriate to the learner's background
- Developing and executing the research project with one-to-one mentor support
- Research communication: writing, structure, and presenting findings clearly
- Guidance on graduate school applications and workshop submissions where relevant
- 1 Preliminary discussion to assess readiness and identify research direction
- 2 Structured literature reading phase with guidance from mentor
- 3 Define research question and plan the project
- 4 Execute project with regular one-to-one mentorship sessions
- 5 Write up and communicate findings — typical duration 4 to 6 months
Which programme is right for you?
Read the prerequisites column carefully — it is the most important one.
| Feature | Mathematics for AI | Generative AI & Diffusion | AI Research Pathway |
|---|---|---|---|
| Price (฿) | 2,700 | 5,600 | 8,200 |
| Typical duration | 6 – 8 weeks | ~10 weeks | 4 – 6 months |
| Prior ML experience needed | No | Yes | Substantial |
| One-to-one mentorship | |||
| Instructor support included | |||
| Paper-based content | |||
| Original research project | |||
| Applied coding exercises |
Standards that apply across all programmes
Data privacy
Learner data is not shared with third parties for marketing. See our Privacy Policy for the full details of how information is handled.
Source transparency
All course materials cite their sources. Learners can verify any claim made in the programme content and follow up on the underlying literature independently.
Instructor-led support
Learner questions are handled by the instructors responsible for each programme. Responses typically arrive within one working day during Bangkok office hours.
Regular review cycle
Each programme is reviewed twice annually. Revisions are made when the field has developed in ways that affect what working knowledge of the subject requires.
Accurate descriptions
Programme prerequisites and scope are described accurately. We do not overstate what a programme delivers or understate the prior knowledge it requires.
All-inclusive pricing
The listed price includes all materials, exercises, and support for the programme as described. There are no add-on fees or tiered access requirements.
Programme fees
All prices in Thai Baht. Each fee includes everything described in the programme.
Mathematics for AI
- All module materials
- Applied exercises
- Instructor support
- Self-paced access
Generative AI & Diffusion
- Paper-grounded lessons
- Framework-based exercises
- Instructor support
- Self-paced access
AI Research Pathway
- Literature reading guidance
- Original research project
- One-to-one mentorship
- Research communication guidance
Not sure which programme fits your current level?
We are happy to discuss your background and help you identify the right starting point. A short conversation is usually enough to clarify the decision.
Send an Enquiry