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Intelligent Systems: Reasoning and Recognition - 4MMSIRR6

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  • Number of hours

    • Lectures : 15.75
    • Tutorials : 15.75
    • Laboratory works : -
    • Projects : -
    • Internship : -
    • Written tests : -
    ECTS : 3.0
  • Officials : James CROWLEY

Goals

This course provides an introduction to techniques for constructing systems that exhibit human level intelligence. The first half of the class covers techniques for machine learning and pattern recognition, including Bayesian learning, artificial neural networks, unsupervised learning and support vector machines. The second half of the class covers techniques for symbolic reasoning systems including planning and problem solving, diagnostic reasoning, rule based programming, reasoning with temporal and spatial relations, and causal reasoning with Bayesian networks.

Programming exercises are performed in Python and the NASA CLIPS symbolic reasoning programming environment.

All lectures are taught in English.

Content

1. Introduction to intelligent systems.
2. Performance Evaluations
3. Bayesian Machine learning
4. Non supervised learning and clustering with K-means and EM
5. Artificial Neural Networks and deep learning.
6. Decision Trees and random forests.
7. Planning and problem solving.
8. Rule based systems
9. Reasoning with spatial, temporal and other forms of relations.
10. Diagnostic and Causal reasoning with Bayesian networks and probabilistic graph models.

Prerequisites

Basic notions of Symbolic Logic
Basic techniques from Probability and statistics
Linear Algebra
Object oriented programming.

Tests

3 Hour written exam, all documents and course notes authorised.

N1=0.9 x E1 + 0.1 x TP
N2=0.9 x E2 + 0.1 x TP

The exam is given in english only FR

Calendar

The course exists in the following branches:

  • Curriculum - Information Systems Engineering - Semester 8 (this course is given in english only EN)
see the course schedule for 2020-2021

Additional Information

Course ID : 4MMSIRR6
Course language(s): FR

The course is attached to the following structures:

You can find this course among all other courses.

Bibliography

  • Polycopié du cours / Course Notes
  • I. Goodfellow, Y. Bengio, and A. Courville. Deep learning. MIT press, 2016.
  • S. Russell, and P. Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited, 2016.

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Date of update January 15, 2017

Université Grenoble Alpes