Brain-like computation and intelligence

Summary

Recent advances in machine learning have contributed to the emergence of powerful models of intelligence. In this course we will compare the behavior and underlying mechanisms between these models and biological brains.

Content

Keywords

NeuroAI, Deep Learning, Perception, Cognition, Behavior, Motor Control and Learning, Python

Learning Prerequisites

Recommended courses

CS-433 (strongly recommended)

Important concepts to start the course

Programming in Python, good mathematical background

Learning Outcomes

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

• Formulate models of brain function
• Hypothesize about potential mechanisms that give rise to behavior
• Design models of brain functions
• Characterize the models

Transversal skills

• Demonstrate the capacity for critical thinking
• Summarize an article or a technical report.
• Write a scientific or technical report.
• Set objectives and design an action plan to reach those objectives.

Teaching methods

Lectures and exercises to discuss and work on problem sets (both numerical and analytical). There will be one project as part of this class, which is partially done outside of the classroom.

Expected student activities

Attend lectures and take notes, participate in quizzes, the modeling project and read scientific articles. Complete problem sets and take the final exam.

Assessment methods

The final mark is a combination of three evaluations: modeling project, quizzes, final exam.

Supervision

Resources

Moodle Link

• https://go.epfl.ch/NX-414