Machine Learning and Neural Networks
1. Introduction to Machine Learning
2. Machine Learning Basics and Classifiers
3. Supervised classifiers and performance metrics
4. Preprocessing steps and pipelines
5. Machine Learning Basics
6. Neural Networks: Biological background
7. NN: Hopfield Networks and Boltzmann Machines
8. Competitive Learning
9. Multi-layer perceptron and gradient optimization
10. Spiking Neural Networks and NeuroEvolution
11. Deep NN: Introduction, Applications. Convolutional NN
12. Recurrent Neural Networks
13. DNNs: Autoencoders and Generative Models
14. Adversarial Examples
15. Deep Belief Networks and Deep Boltzmann Machines