Every time a new MSc. or PhD candidate comes to the lab, one of the first questions they ask is

“Which courses should I take?”

This post aims to summarize this information once and for all. The CAD lab main research fields are detailed on the lab’s website .

The courses are sorted according to category and the different departments are represented by the numbers (03… = Mechanical engineering, 23… = Computer science, 04… = Electrical engineering).

CAD, Geometry and 3D

• 035003  – Cad Systems 1 (Undergraduate)
• 036020  – Computational Geometry 1
• 036045  – Computational Geometry and Cad Models 2
• 049051  – Advanced Topics in Computer Graphics
• 049056  – Topics in Computer Vision# Shape Analysis (Parallel to 049051)
• 236629 – Advanced Topics in Computer Graphics (vector fields)

Computer Vision

• 046746  – Algorithms and Application in Computer Vision
• 236873  – Computer Vision (Parallel to 046746)
• 236815  – Seminar in Computer Vision
• 048921  – Visual Systems and Image Structure

Machine Learning

• Coursera –  Machine learning course (Given by Andrew Ng)
• 236501    – Introduction to Artificial Intelligence
• 097209    – Machine Learning and Online Optimization
• Coursera – Neural Networks for Machine Learning (Given by Geoffrey Hinton)

Miscellaneous (Algorithms, Optimization etc.)

• 036044  – Robot Path-Planning and Sensor Based Navigation
• 019136  – Optimal Structural Design

Recommended Course Order

In general you can take the courses listed above in any given order. However, some courses are a better starting point than others. The following courses span the fundamental knowledge required for an advanced degree in my field of research.

1. 035003  – Cad Systems 1 (Undergraduate)
   • Computer graphics and CAD
   • Code: MATLAB
   • Time Requirement:  High
   • Difficulty: Easy
2. 046746  – Algorithms and Application in Computer Vision
   • Computer vision tools
   • Code: MATLAB
   • Time Requirement:  High
   • Difficulty: Medium+
3. 236501    – Introduction to Artificial Intelligence
   • Machine learning tools
   • Code: Python
   • Time Requirement:  Medium
   • Difficulty: Medium
4. Search in categories according to research subject

Special thank to Gil Elbaz and Roi Shikler for helping to compose this list.