Cursus IA
cursus IA informatique note-ébauche
Livres
Algorithmique générale
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💻️ SEDGEWICK, Robert, WAYNE, Kevin. (2014). Algorithms, Part I. Princeton University. [~54h]
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💻️ SEDGEWICK, Robert, WAYNE, Kevin. (2014). Algorithms, Part II. Princeton University. [~62h]
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💻️ ROUGHGARDEN, Tim. (2017). CS161: Algorithms Specialization. Stanford University. [~59h]
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💻️ DEMAINE, Erik, KU, Jason, SOLOMON, Justin. (2020, Spring). 6.006: Introduction to Algorithms. MIT. [~56h]
ou
💻️ DEMAINE, Erik, DEVADAS, Srini. (2011, Fall). 6.006: Introduction to Algorithms. MIT. (Demaine 2011 est célèbre ; quid du 2020 ?) -
💻️ DEMAINE, Erik, DEVADAS, Srini, LYNCH, Nancy. (2015, Spring). 6.046J: Design and Analysis of Algorithms. MIT. [~72h]
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📖 Competitive Programming 3
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📖 Guide to Competitive Programming: Learning and Improving Algorithms Through Contests
Algorithmique IA
- 📖 RUSSELL, Stuart, NORVIG, Peter. (2021). Artificial Intelligence: A Modern Approach (4th global ed.). Pearson. [~1168p]
- 📖 GOODFELLOW, Ian, BENGIO, Yoshua, COURVILLE, Aaron. (2016). Deep Learning. MIT Press. [~800p]
Mathématiques appliquées
- 📖 DEISENROTH, Marc P., FAISAL, A. Aldo, SOON HONG, Chen. (2020). Mathematics for Machine Learning. Cambridge University Press. [~390p]
- 📖 BISHOP, Christopher M. (2011). Pattern Recognition and Machine Learning (2nd ed.). Springer. [~738p]
- 📖 JAMES, Gareth, WITTEN, Daniela, HASTIE, Trevor, TIBSHIRANI, Robert Tibshirani. (2021). An Introduction to Statistical Learning: with Applications in R (2nd ed.). Springer. [~607p]
- 📖 HASTIE, Trevor, TIBSHIRANI, Robert, FRIEDMAN, Jerome. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer. [~745p]
MOOC
- 💻️ RUSSELL, Stuart (2023, Spring). CS188: Introduction to Artificial Intelligence. University of California, Berkeley. [~120h]
- 💻️ ABU-MOSTAFA, Yaser. (2012). CS1156: Learning from Data. Caltech. [~90h]
- Compléter avec CS229 de stanford (ANdrew Ng) https://www.quora.com/Which-machine-learning-course-CS-229-Stanford-or-CS1156-Caltech-should-I-take-after-finishing-the-Stanford-Coursera-machine-learning-course
Python
D’après mes recherches, les deux meilleurs livres pour maîtriser Python à un niveau expert sont :
Bible de base niveau moyen à avancé :
- 📖 ERNESTI, Johannes, Kaiser Peter. (2022). Python 3: The Comprehensive Guide. Rheinwerk Computing.
https://www.goodreads.com/book/show/75431536-python-3
Johannes Ernesti is research scientist at DeepL. He is a graduate mathematician and received his doctorate in applied mathematics from the Karlsruhe Institute of Technology (KIT).
Peter Kaiser is a research scientist at DeepL. He has a degree in computer science and received a doctorate in humanoid robotics from the Karlsruhe Institute of Technology (KIT).
Bible niveau expert pour créer du code “pythonic” en exploitant toutes les idiosyncrasies du langage :
- 📖 RAMALHO, Luciano. (2022). Fluent Python: Clear, Concise, and Effective Programming (2nd ed.). O’Reilly.
https://www.goodreads.com/book/show/54330869-fluent-python-2nd-edition
Luciano Ramalho is a Principal Consultant at ThoughtWorks and a fellow of the Python Software Foundation. He’s been using Python professionally since 1998, deploying it in some of the largest Internet properties based in Brazil, as well as financial and government institutions. Ramalho has presented Python talks and tutorials in six countries, including events like PyCon US, OSCON, Python Brasil, PyBay, and PyCaribbean. He is co-owner of Python.pro.br, a training company.
En niveau débutant je conseille le cours d’Harvard CS50P dédié à l’aprentissage de Python jusqu’à un niveau moyen, avec le très bon professeur de CS50 (le cours CS50 est plus vaste et enseigne l’algorithmique, plusieurs langages, bases de données, etc.) https://www.edx.org/learn/python/harvard-university-cs50-s-introduction-to-programming-with-python
ou le livre le plus réputé pour débutants :
- 📖 MATTHES, Eric. (2023). Python Crash Course (3rd ed.). No Starch Press.
https://www.goodreads.com/book/show/60704826-python-crash-course-3rd-edition
Les MOOC d’Andrew Ng (professeur à Stanford, Google Brain, deeplearning.ai) sont réputés aussi :
- https://www.andrewng.org/courses/
- https://www.coursera.org/instructor/andrewng
- https://www.deeplearning.ai/courses/
Livre d’intro par Hastie : https://www.statlearning.com/ (avec cours en vidéo)
https://www.r-bloggers.com/2014/09/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
“I wrote a 4000-words long article about all the math you need to know for machine learning. Trust me, you want to bookmark this:” https://x.com/TivadarDanka/status/1955906072058978727
https://thepalindrome.org/
https://thepalindrome.org/p/the-roadmap-of-mathematics-for-machine-learning