Machine Learning and Neural Networks Lectures

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