Fundamentals of Machine Learning - Trappenberg, Thomas

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Résumé :

• 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