Introduction to Artificial Intelligence Course (Introduction a l'Intelligence Artificielle IG2410)

www.ISEP.fr Institut Supérieur d'Electronique de Paris), February 2019.

Logistics:

Course webpage: https://github.com/NataliaDiaz/IntroToAI

Syllabus and Timetable:

Week 1: Introduction to AI, history of AI, course logistics, and roadmap

Week 2: Intelligent agents, informed and uninformed search. Problem solving

Week 3: Heuristic search, greedy search, A* algorithm, stochastic search

Week 4: Adversarial search, game playing

Week 5: Constraint satisfaction problems, Knowledge Representation (expert systems, description logics, knowledge graphs, ontologies and fuzzy ontologies)

Tutorial: Pizzas in 10 minutes - Protege Tutorial

Week 6: Machine Learning 1: basic concepts, linear models, K nearest neighbors, decision trees, overfitting, supervised and unsupervised learning Tutorial on regression: https://nbviewer.jupyter.org/github/mar-one/ACM-Python-Tutorials-KAUST-2015/blob/master/basics/project_linear_regression.ipynb

Week 7: Machine Learning 2: Neural Networks, Markov decision processes, Reinforcement Learning. PyTorch Tutorial Slides: CNN Ingredients: https://docs.google.com/presentation/d/1ZQHChgyv4YjmrVpl77il1FKD-CuYd0gTOLCzwmnS2Sg/edit?usp=sharing

Week 8: Independent project work

Week 9: Independent project work

Week 10: Independent project work

Week 11: Independent project work

Week 12: Independent project work

Week 13: Independent project work

Week 14: Independent project work

Weeks 1-9 will be 1h Class Magistral (CM) + 2h Practical (TP)

Weeks 10-14 will be 3h TD (guided tutorials and project work)

Résumé en Francais:

Planification pour étudiants en A2 (deuxième année cycle ingénieur):

Introduction IA Définition de l'intelligence artificielle.

Résolution de problèmes

Stratégies d'exploration non informées.

Stratégies d'exploration informées

Problèmes à satisfaction de contraintes

Exploration en situation d'adversité (les jeux)

Agents fondés sur les connaissances

Représentation des connaissances et inférence. Systèmes experts.

Apprentissage

Apprentissage supervisé : arbres de décisions, réseaux de neurones.

Apprentissage non-supervisé.

(30 étudiants, 42h de présence (14 semaines x 3hrs), reparties en cours-TD-TP-> 47 total).

Prerequisites

Students are required to have the following prerequisites:

Linear algebra (vectors, matrices, derivatives)
Calculus
Basic probability theory
Python programming

The course will allow students to dive into Python while solving AI problems and learning its applications. Programming assignments will be in Python.

Evaluation:

50% Presenting exercises during the lectures worked during the previous session and week + 50% independent work project

Exercises:

Jupyter notebooks to be corrected the following week. Students presenting solutions will be awarded points: https://github.com/NataliaDiaz/aima-exercises

Tutorials:

1 Python refresher: https://docs.google.com/document/d/1VrJuvq3yNw09qr9NXSrVXovzbj-fT8X4qNkpFdWU9uY/edit?usp=sharing

Project ideas:

Reproducing SPINNING UP tutorials
Migrating them to PyTorch
Implementing a Roomba cleaner simulator (see http://maxbareiss.com/blog/) https://github.com/MaxBareiss/super-roomba-gym (old version not python: https://github.com/MaxBareiss/super-roomba). Real roomba: https://github.com/NickWaterton/Roomba980-Python

What is your project idea?

References:

All Algorithms: http://aima.cs.berkeley.edu/algorithms.pdf

Acknowledgements

Course adapted from:

http://aima.cs.berkeley.edu/ AIMA Course (P. Norvig and S. Russell).

Berkeley Artificial Intelligence course (P. Abbeel and D. Klein).

Other support courses:

https://courses.edx.org/courses/course-v1:ColumbiaX+CSMM.101x+2T2017/course/,
Hinton's MOOC "Neural Networks for Machine Learning" prepared in 2012 and is now seriously out of date; "discontinued but the lectures are still a good introduction to many of the basic ideas" https://www.cs.toronto.edu/~hinton/coursera_lectures.html
https://courses.edx.org/courses/course-v1:ColumbiaX+CSMM.101x+2T2017/course/
Related course at ENSTA: https://synapses.ensta-paristech.fr/catalogue/2017-2018/ue/1650/INT23-introduction-a-l-intelligence-artificielle

Books

1 - Artificial Intelligence: A Modern Approach (Third edition) by Stuart Russell and Peter Norvig http://aima.cs.berkeley.edu

2 - Grokking Deep Learning by Andrew Trask: https://www.manning.com/books/grokking-deep-learning and notebooks: https://github.com/iamtrask/Grokking-Deep-Learning Very recommended to get the basis of deep learning before committing to learn any framework (basics in numpy). If you want a discounted book, ask me.