CS-503 / 5 crédits

Enseignant: Roshan Zamir Amir

Langue: Anglais


Summary

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

Content

Keywords

Computer vision, machine learning, cognition, embodied intelligence, robotics, neural networks, AI.

Learning Prerequisites

Required courses

Introduction to Machine Learning (CS-233) or Machine Learning (CS-433) or equivalent course on the basics of machine learning and deep learning.

Recommended courses

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

Important concepts to start the course

  • Python programming.
  • Basics of deep learning and machine learning.
  • 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 and demonstrate continuous progress throughout the semester.

Assessment methods

  • Project (70%) [Project proposal, Project checkpoint reports, Final project report and presentation]
  • Homeworks (20%)
  • Class attendance and engagement (10%)

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

Ressources en bibliothèque

Notes/Handbook

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

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Visual intelligence : machines and minds
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Visual intelligence : machines and minds
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Visual intelligence : machines and minds
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Visual intelligence : machines and minds
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Visual intelligence : machines and minds
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Visual intelligence : machines and minds
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11   INJ218 
11-12    
12-13     
13-14     
14-15 INF2   
15-16    
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Mardi, 14h - 16h: Cours INF2

Jeudi, 10h - 12h: Exercice, TP INJ218