There had been remarkable growth in applications of machine learning and AI due to plethora of open source materials and software packages available in internet. Following are few materials on the basics of neural networks, deep learning and artificial intelligence that you can find in internet and more will be updated subsequently. The purpose of this page is to bring materials for an upcoming course on data science.
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Neural networks : Basic ideas on neural network , gradient descent , back propagation , backpropagation calculus in amazing videos made by 3blues1brown youtube channel. Check their other videos on cool math stuff.
Generative Adversarial Network (GAN) : The seminal paper by Goodfellow et al. (2014) and some of its theoretical properties.
Deep learning : A critique from statistician's side:
Federated Learning & Distributed computing : Clustered federated learning.
Few interesting links:
Simpson's Paradox : American Statistician 2014 , A nice A/B testing interpretation , What is it
Why regularization: Sensitivity Bias - variance trade off
Data enrichment : Linear regression contexts
How to define confounding: To begin with
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