Computer Vision

principles and practice

A math-first approach to learning computer vision in a classroom setting

Computer Vision

principles and practice

A math-first approach to learning computer vision in a classroom setting

  • Quality Classroom

    We pay meticulous attention to the logistical details of our workshops: quality audio and visual setups, comfortable sitting arrangements, small group size, large displays and extra monitors. Dinners are included for evening workshops and lunch for afternoon workshops.

  • Workshop Lecturer’s Notes

    Including Course Book (PDF), HTML files, course transcripts (if any).

  • Highly-accelerated Learning

    Learn under the assistance of our lead instructor and a band of qualified teaching assistants throughout the 2 – day course.

  • Certification of Completion

    Show current and prospective employers that you’ve completed the course with a signed certificate of completion.

Testimonials from previous workshop

The intended audience for this course and accompanying workshop are absolute beginners who have an interest in computer vision and the application of artificial intelligence in modern day CV libraries. Some familiarity in Python and / or geometry will be helpful but is not required as the instructor assumes no prior math / programming knowledge for all workshops in the Classroom series.

The lecture is carried out by a Lead Instructor, and adopts a learn-by-building approach. Participants are required to bring their personal laptop to follow each section and write code to solve small challenges procedurally.


  • Affine Transformation
    • Definition
      • Mathematical Definitions
    • Practical Examples
    • Motivation
    • Getting Affine Transformation
      • Trigonometry Proof
    • Code Illustrations
    • Summary and Key Points
    • Optional video
      • Rotation Matrix Explained Visually
    • References and learn-by-building modules
  • Kernel Convolutions
  • Definition
    • Optional video
      • Kernel Convolutions Explained Visually
    • Mathematical Definitions
    • Padding
  • Smoothing and Blurring
  • A Note on Terminology
    • Kernels or Filters?
    • Correlations vs Convolutions?
  • Code Illustrations: Mean Filtering
  • Role in Convolution Neural Networks
  • Handy Kernels for Image Processing
    • Gaussian Filtering
    • Sharpening Kernels
    • Gaussian Kernels for Sharpening
    • Unsharp Masking
  • Summary and Key Points
  • References and learn-by-building modules
  • Edge Detection
  • Definition
  • Gradient-based Edge Detection
    • Sobel Operator
      • Discrete Derivative
      • Code Illustrations: Sobel Operator
    • Gradient Orientation & Magnitude
  • Image Segmentation
    • Intensity-based Segmentation
      • Simple Thresholding
      • Adaptive Thresholding
    • Edge-based Contour Estimation
      • Contour Retrieval and Approximation
    • Canny Edge Detector
      • Edge Thinning
      • Hysteresis Thresholding
  • References and learn-by-building modules
  • Attempt a 7-question quiz in class
  • Attempt all learn-by-building modules in the coursework
  • Successful attempts are rewarded with a badge
  • All participants are given a certificate of completion

The workshop will be delivered in English. Participants will learn in a classroom setting by following the lecture with a personal laptop.

Course Material Preview

Course Preview


Samuel Chan

An  RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce.  Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.

Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.

Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.