V
Vectorise
Mathematical topology surface
Kuala Lumpur · AI Education · en_MY

Where AI Theory
Meets Genuine Practice

Vectorise offers structured, academically-grounded programmes in machine learning, speech processing, and mathematical optimisation. For practitioners who want to understand the mechanics, not just run the pipelines.

Enrol in a Programme View Courses
+60 3 2098 4317 [email protected] Weekly live sessions Instructor review included
Current Programmes · 2026

Three Learning Regions

Each course occupies a distinct conceptual territory. They can be taken independently or sequenced for a broader mathematical foundation in AI.

Probabilistic Machine Learning
14 Weeks
MYR 4,100
Programme 1

Probabilistic Machine Learning

Examines ML through a probabilistic framework — covering Bayesian inference, graphical models, variational methods, Monte Carlo approaches, and Gaussian processes. Students implement key methods from scratch.

  • PyMC & NumPy implementation track
  • Uncertainty quantification focus
  • Weekly live discussion sessions
  • Prerequisites: probability, linear algebra, prior ML exposure
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Applied Speech Processing
12 Weeks
MYR 3,400
Programme 2

Applied Speech Processing

Covers audio signal processing fundamentals, traditional speech recognition approaches, modern end-to-end architectures, text-to-speech systems, and evaluation methodology. Projects span recognition and synthesis tasks.

  • PyTorch and established speech libraries
  • Recognition and synthesis projects
  • Instructor review of submissions
  • Prerequisites: deep learning familiarity
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Foundations of Optimization
10 Weeks
MYR 2,700
Programme 3

Foundations of Optimization

Focuses on optimization as applied in ML contexts: convex fundamentals, gradient-based methods and variants, second-order approaches, constrained optimization, and non-convex topics relevant to neural network training.

  • Theory balanced with implementation
  • Non-convex optimization for deep learning
  • Suitable as preparation for advanced ML
  • Prerequisites: calculus and linear algebra
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Why Vectorise

What Sets the Curriculum Apart

Depth Over Surface

Each programme addresses the underlying mechanics — not just APIs. Students who complete the work understand why their models behave as they do.

Implementation From First Principles

Key methods are built from scratch before any library abstractions. This builds durable understanding rather than tool dependency.

Live Weekly Sessions

Not recorded replays presented as live. Substantive reading discussions and Q&A each week with the instructional team.

Instructor Project Review

Submitted work receives written feedback. Not an automated pass/fail scoring system — actual review of reasoning and implementation choices.

Coherent Prerequisite Chain

Courses are sequenced so each builds on prior knowledge. Clear prerequisites mean students enter prepared and progress without hidden gaps.

Manageable Weekly Commitment

Designed for working practitioners. The weekly pace is demanding but realistic for those with other professional obligations.

Ready to Begin?

Is Your Next Programme Here?

Each Vectorise course intake is small by design — enough students for substantive discussion, few enough for individual attention. Reach out to confirm availability for the current intake.

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+60 3 2098 4317 [email protected]
Common Questions

Frequently Asked

What prior knowledge is needed before enrolling?
Requirements differ per programme. Probabilistic ML assumes solid probability, linear algebra, and prior ML exposure. Applied Speech Processing requires deep learning familiarity. Foundations of Optimization asks for calculus and linear algebra. Before enrolling, you are expected to honestly assess whether you meet those requirements — the courses do not reteach foundational material in depth.
Are sessions recorded if I miss a live meeting?
Live sessions are the core of the weekly rhythm. Recordings may be made available for review, but they are not a substitute for attendance. If your schedule cannot accommodate the weekly session time, this programme structure may not be the right fit.
How much time per week should I set aside?
Expect 8–12 hours per week across readings, implementation work, and the live session. Some weeks will be lighter; others — especially those with project submissions — will be heavier. The programmes are built for working professionals with real time constraints, but they do require consistent weekly effort.
What does instructor project review actually involve?
Submitted projects receive written feedback on both the reasoning and the implementation. This is not a rubric-scored auto-grade. The instructor reads the work and responds to specific decisions you made. Turnaround time is typically within one week of submission.
What is the payment structure?
Full course fees are stated per programme in Malaysian Ringgit (MYR). Payment details and instalment options where available are confirmed upon enrolment. Contact us for specifics before committing.
Can courses be taken in any order, or is there a recommended sequence?
Foundations of Optimization is a sensible starting point if your mathematical background needs consolidation. Probabilistic ML and Applied Speech Processing can follow. Each course stands independently for students who already meet its prerequisites, but they are designed to complement each other well.
Location Coordinate

Find Us in Kuala Lumpur

Level 7, Bangunan Securities Commission, 3 Persiaran Bukit Kiara, 50490 Kuala Lumpur

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Contact Details

Address Level 7, Bangunan Securities Commission
3 Persiaran Bukit Kiara
50490 Kuala Lumpur
Working Hours (MYT) Mon–Fri: 9:00 AM – 6:00 PM
Saturday: 10:00 AM – 2:00 PM
Sunday: Closed

Course intakes are limited. If you are close to meeting the prerequisites and would like to discuss readiness, we are happy to help you assess fit before committing.

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