Course in Machine Learning (Classical and Quantum)

In this class we will cover the basic of regression and supervised classification, both theoretical notions (not so much)
and numerical examples. We will create the models from scratch, to give ideas of what we are doing and why. We will
cover neural networks and also support vector machines. These models are at the very core of machine learning.

2.1 Neural networks
  2.1.1 What are neural networks
  2.1.2 Types of neural networks
  2.1.3 Universality of neural networks
  2.1.4 Training of neural networks: backpropagation
  2.1.5 Creating our own neural network

2.2 Support vector machines
  2.2.1 Kernel trick
  2.2.2 Classification of points
  2.2.3 Creating our own support vector machine