Visual intelligence : machines and minds

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

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

• Animal Eyes / Land
• An immense world / Yong (added)
• Vision Science / Palmer
• Ecological Approach to Visual Perception / Gibson
• Computer Vision / Szeliski

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

• https://go.epfl.ch/CS-503