CS-503 / 6 credits

Teacher: Roshan Zamir Amir

Language: English


Summary

The course will discuss classic material as well as recent advances in computer vision and machine learning relevant to processing visual data -- with a primary focus on embodied intelligence and vision for active agents.

Content

Keywords

Computer vision, Machine learning, Embodied intelligence, Robotics, Cognition, Neural networks, AI.

Learning Prerequisites

Required courses

  • Machine Learning (CS-433) or Introduction to Machine Learning (CS-233) or equivalent course on the basics of machine learning.
  • Deep Learning (EE-559) or Artificial Neural Networks (CS-456) or equivalent course on the basics of deep learning.

Recommended courses

  • Computer vision (CS-442) or equivalent undergraduate/masters course on the basics of computer.

Important concepts to start the course

  • Deep learning and machine learning.
  • Python programming.
  • Basics of probability and statistics.

Learning Outcomes

By the end of the course, the student must be able to:

  • Define the basic concepts in computer vision, such as detection, segmentation, 3D from X, covered in the lectures.
  • Explain the range of theories in psychology around visual perception, covered in the lectures.
  • Design and implement computer vision/machine learning algorithms to address problems with real-world complexity.
  • Design and implement proper evaluation pipelines for computer vision/machine learning algorithms to assess their performance in the real-world.
  • Assess the limits and performance pitfalls of a given computer vision/machine learning algorithm, especially when facing real-world complexity

Transversal skills

  • Write a scientific or technical report.
  • Make an oral presentation.
  • Assess progress against the plan, and adapt the plan as appropriate.
  • Demonstrate the capacity for critical thinking

Teaching methods

Lectures. Lab sessions. Project Tutoring. Course Project.

Expected student activities

  • In regard to the lectured material, the students are expected to study the provided reading material, actively participate in the class, engage in the discussions, and answer homework questions.
  • In regard to the course project, the students are expected to formulate and implement an in-depth project, demonstrate continuous progress throughout the semester, and provide a final written report and presentation.

Assessment methods

  • Project (70%) [distributed over the project proposal, milestone reports, final report and presentation]
  • Homeworks (30%)

Supervision

Office hours Yes
Assistants Yes
Forum Yes

Resources

Bibliography

  • Vision Science: Photons to Phenomenology, Steven Palmer, 1999.
  • The Ecological Approach to Visual Perception, Jame Gibson, 1979.
  • Computer Vision: Algorithms and Applications, Richard Szeliski, 2020.
  • Animal Eyes, Michael Land and Dan-Eric Nilsson, 2012.

Ressources en bibliothèque

Notes/Handbook

The reference reading of different lectures will be from different books (the main ones listed above) and occasionally from papers. Resources will be provided in class. Full-text books are not mandatory.

Moodle Link

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Visual intelligence : machines and minds
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11   INM202 
11-12    
12-13     
13-14     
14-15     
15-16     
16-17   INM202 
17-18    
18-19     
19-20     
20-21     
21-22     

Thursday, 10h - 12h: Exercise, TP INM202

Thursday, 16h - 18h: Lecture INM202