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