Our Courses
Three courses. Each serious about its subject.
We offer structured, paced courses in three areas of AI development — each designed to build a working foundation rather than a surface familiarity.
← Back to HomeHow Every Course Is Built
Our teaching methodology
Each Khun Academy course is structured as a reading-and-exercise sequence — a weekly rhythm of new material followed by problem sets, followed by feedback, followed by the next block. This structure is deliberate. Spaced practice and retrieval are known to improve retention; we build them into the course architecture so they are not optional.
Before each intake, students receive a short background assessment. This is not an entrance exam. It is a way for us to understand where a student is and to identify any gaps that might cause difficulty. We use this information to tailor early feedback and to flag concepts that may need extra attention.
Mentors are active throughout each course, not only at project submission. If a student's problem set suggests a misconception, the feedback addresses it. If an assignment demonstrates solid understanding, the feedback confirms this and often suggests directions to explore further.
Course 01
Statistical Foundations for AI
A course for learners who want a careful, working grounding in the statistical concepts that underpin practical machine learning — probability, estimation, hypothesis testing, and the reasoning behind common evaluation metrics. The course is paced as a careful reading-and-exercise sequence with weekly problem sets and a closing analysis project. Suited for learners with prior college-level mathematics willing to spend six hours per week over ten weeks.
- Probability theory — foundations through to Bayesian updating
- Estimation: point estimates, intervals, and their properties
- Hypothesis testing, p-values, and significance in context
- Evaluation metrics for classification and regression models
- Closing analysis project on a real dataset
Course sequence
Course 02
Computer Vision Track
A focused track covering the practical foundations of computer vision — image preprocessing, classical features, convolutional architectures, and the typical workflow of building a vision model for a specific task. The track includes weekly hands-on assignments and a substantial final project that learners scope in consultation with a mentor. Suited for learners who have completed prior coursework in deep learning fundamentals.
- Image data handling, preprocessing, and augmentation
- Classical feature extraction: edges, keypoints, descriptors
- Convolutional neural network architectures in depth
- Full model-building workflow for a real task
- Mentor-guided final project with substantive review
Track sequence
Course 03
Reinforcement Learning Programme
A long-form programme covering reinforcement learning from foundations through to recent practical approaches — Markov decision processes, value-based and policy-based methods, and an introduction to current research directions. The programme includes structured weekly content, periodic small projects, and a closing capstone project. Suited for learners willing to commit twelve hours per week over twenty weeks. Mentor review is included throughout.
- Markov decision processes and the RL problem formulation
- Value-based methods: Q-learning through deep Q-networks
- Policy gradient methods and actor-critic approaches
- Model-based RL and current research directions
- Closing capstone project with full mentor review
Programme structure
Course Comparison
Which course fits your situation?
|
Statistics
฿4,200
|
Computer Vision
฿16,500 · Popular
|
RL Programme
฿32,500
|
|
|---|---|---|---|
| Duration | 10 weeks | Focused track | 20 weeks |
| Weekly commitment | ~6 hours | ~8 hours | ~12 hours |
| Mentor review | |||
| Closing project | |||
| Periodic small projects | — | — | |
| Prerequisites | College maths | Deep learning basics | Strong ML background |
| Best for | Building the mathematical foundation | Entering a specific applied domain | Deep engagement with a complex field |
Shared Standards
What applies to all three courses
Privacy and data handling
Student submissions and personal information are held securely and never shared with third parties. We comply with Thailand's Personal Data Protection Act (PDPA).
Background assessment
Every enrolled student completes a short pre-course assessment. We use this to calibrate feedback and identify areas that may need early attention.
Annual content review
All three courses are reviewed annually. Material that has become outdated — particularly relevant to the RL programme — is updated before each new intake.
Responsive instructor support
Questions sent to your instructor receive a response within two business days. For urgent matters, phone and email contact is available during working hours.
English-language instruction
All materials and mentor communication are in English, reflecting the language in which most primary AI research is published.
All-inclusive pricing
The stated course fee covers all materials, assignments, project review, and mentor engagement for the full duration. No add-ons or additional charges.
Enrolment Pricing
Course fees (Thai Baht)
All fees are stated in Thai Baht. Payment is required before the course start date.
Course 01
Statistical Foundations
- 10 weeks of structured content
- Weekly problem sets with feedback
- Closing analysis project
- All materials included
Course 02 · Popular
Computer Vision Track
- Full track with weekly assignments
- Hands-on coding exercises
- Mentor-guided final project
- All materials included
Course 03
Reinforcement Learning
- 20 weeks with full mentor access
- Periodic small projects throughout
- Closing capstone project
- All materials included
Not sure which course?
Tell us where you are and we will help you figure out where to start
Send a note with your background and what area of AI you are hoping to work in. We will give you a straightforward recommendation.
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