Fundamentals of Machine Learning

1. Introduction 1.1: The basic idea and history of Machine Learning 1.2: Mathematical formulation of the basic learning problem 1.3: Nonlinear regression in highdimensions 1.4: Recent advancements 1.5: No free lunch I A PRACTICAL GUIDE TO MACHINE LEARNING 2. Scientific programming with Python 2.1: Programming environment 2.2: Basic language elements 2.3: Code efficiency and vectorization 2.4: Data handling 2.5: Image processing and convolutional filters 3. Machine learning with sklearn 3.1: Classification with SVC, RFC and MLP 3.2: Performance measures and evaluations 3.3: Data handling 3.4: Dimensionality reduction, feature selection, and tSN 3.5: Decision Trees and Random Forests * 3.6: Support Vector Machines (SVM) * 4. Neural Networks and Keras 4.1: Neurons and the threshold perceptron 4.2: Multilayer Perceptron (MLP) and Keras 4.3: Representational learning 4.4: Convolutional Neural Networks (CNNs) 4.5: What and Where 4.6: More tricks of the trade II FOUNDATIONS: REGRESSION AND PROBABILISTIC MODELING 5. Regression and optimization 5.1: Linear regression and gradient descent 5.2: Error surface and challenges for gradient descent 5.3: Advanced gradient optimization (learning) 5.4: Regularization: Ridge regression and LASSO 5.5: Nonlinear regression 5.6: Backpropagation 5.7: Automatic differentiation 6. Basic probability theory 6.1: Random numbers and their probability (density) function 6.2: Moments: mean, variance, etc. 6.3: Examples of probability (density) functions 6.4: Some advanced concepts 6.5: Density functions of multiple random variables 6.6: How to combine prior knowledge with new evidence 7. Probabilistic regression and Bayes nets 7.1: Probabilistic models 7.2: Learning in probabilistic models: Maximum likelihood estimate 7.3: Probabilistic classification 7.4: MAP and Regularization with priors 7.5: Bayes Nets: Multivariate causal modeling 7.6: Probabilistic and Stochastic Neural Networks 8. Generative Models 8.1: Modelling classes 8.2: Supervised generative models 8.3: Naive Bayes 8.4: Unsupervised generative models 8.5: Generative Neural Networks III ADVANCED LEARNING MODELS 9. Cyclic Models and Recurrent Neural Networks 9.1: Sequence processing 9.2: Simple Sequence MLP and RNN in Keras 9.3: Gated RNN and attention 9.4: Models with symmetric lateral connections 10. Reinforcement Learning 10.1: Formalization of the problem setting 10.2: Modelbased Reinforcement Learning 10.3: Modelfree Reinforcement Learning 10.4: Deep Reinforcement Learning 10.5: Actors and actorcritics 11. AI, the brain, and our society 11.1: Different levels of modeling and the brain 11.2: Machine learning and AI 11.3: The impact machine learning technology on society