This section refers to machine learning / statistical learning.

Getting into AI for non-technical people: https://www.notion.so/Getting-into-AI-for-non-technical-people-42551c69cd604960bf683b9b66dd37d0

Getting into AI for technical people: https://www.notion.so/Getting-into-AI-for-technical-people-b1fbaa983c2a4ed7948248e3eae9f9a0

Theory

• Read An Introduction to Statistical Learning with Applications in R (Hastie et al)
• Read (more difficult) Elements of Statistical Learning (Hastie et al)
• Information Theory, Learning and Learning Algorithms (David MacKay)
• Machine Learning: A Probabilistic Perspective (Kevin Murphy)
• Pattern Recognition and Machine Learning (Christopher Bishop, 2007)

Applications

Python:

• Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Aurelie Gerón)
• Read Data Science from Scratch (Joel Grus)

R

• R for Data Science (Hadley Wickham)

MOOCS

• Andrew Ng Stanford CS229 Machine Learning 2018: https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU

AI for everyone : https://www.coursera.org/learn/ai-for-everyone

• Bloomberg : https://bloomberg.github.io/foml/#lectures

• FastAI: https://www.fast.ai/2020/08/21/fastai2-launch/

Online Articles

Elements of AI free online course: https://www.elementsofai.com/

Mining of Massive Datasets: http://www.mmds.org/

Support Vector Machines (SVM) : https://www.svm-tutorial.com/

Machine Learning from Sratch : https://dafriedman97.github.io/mlbook/content/introduction.html

05/09/2020