## Summary

This course covers the fundamentals of machine learning and deep learning for data scientists.
Specifically, the first half covers foundations of machine learning (probability, MLE, gradient descent, overfitting, regularization, etc.) and basic supervised models.
In the second half (deep learning part), basic structure of neural networks and backpropagation will be discussed, followed by details and applications of convolutional and recurrent neural networks.