Memory models and Learning in Natural Systems - WPMGMMA7

Informations générales

  • Number of hours

    • Lectures 9.0
    • Projects 0
    • Tutorials 9.0
    • Internship 0
    • Laboratory works 0

    ECTS

    ECTS 1.5

Goal(s)

The purpose of this course is to present two points of view of learning, (i) with respect to modeling in cognitive psychology and (ii) with respect to modeling in machine learning.

Contact Stephane ROUSSET, Marion DOHEN

Content(s)

The course is divided into two main parts:
Human & Artificial Memory: This part of the course will present how distributed memorization instantiated by certain artificial neural networks allows us to propose new avenues for modeling human memory.
Machine Learning: This part of the course will present the connections between machine learning, statistics, logic, and artificial intelligence. In addition to the foundations of the field and the emphasis on the intelligibility/efficiency tradeoff, several symbolic learning algorithms will be presented in this course in relation to traditional development environments: R, Weka, Orange, etc.



Prerequisites

Test

SESSION 1: In-person
Exam procedures: Two written exams (Exam 1 and Exam 2, one per module), with a time limit (1 hour each, one after the other).
Authorized documents: No documents for Exam 1 (Rousset) and a handwritten A4 sheet of paper for Exam 2 (Torlay).
Grade calculation: 50% Exam 1 + 50% Exam 2

SESSION 2: In-person
Exam procedures: Two written exams (Exam 1 and Exam 2, one per module), with a time limit (1 hour each, one after the other).
Authorized documents: No documents for Exam 1 (Rousset) and a handwritten A4 sheet of paper for Exam 2 (Torlay).
Grade calculation: 50% Exam 1 + 50% Exam 2

SESSION 1: Remote
Exam procedures: Two written exams (Exam 1 and Exam 2, one per module) or online questionnaire. Online with authentication if available, limited time (1 hour each)
Authorized documents: No documents for Exam 1 (Rousset) and a handwritten A4 sheet of paper for Exam 2 (Torlay)
Grade calculation: 50% Exam 1 + 50% Exam 2

Remote SESSION 2
Exam methods: Two written exams (Exam 1 and Exam 2, one per module) or online questionnaire with authentication if available, limited time (1 hour each)
Authorized documents: No documents for Exam 1 (Rousset) and a handwritten A4 sheet of paper for Exam 2 (Torlay)
Grade calculation: 50% Exam 1 + 50% Exam 2



Additional Information

Course list
Curriculum->Double-Diploma Engineer/Master->Semester 9
Curriculum->Master->Semester 9