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Book Description

Table of Contents

- About this Book
- How to Use this Book
- Why Another Textbook?
- Organization and Auxilary Material
- Acknowledgements

- Decision Trees
- What Does it Mean to Learn?
- Some Canonical Learning Problems
- The Decision Tree Model of Learning
- Formalizing the Learning Problem
- Inductive Bias: What We Know Before the Data Arrives
- Not Everything is Learnable
- Underfitting and Overfitting
- Separation of Training and Test Data
- Models, Parameters and Hyperparameters
- Chapter Summary and Outlook
- Exercises

- Geometry and Nearest Neighbors
- From Data to Feature Vectors
- K-Nearest Neighbors
- Decision Boundaries
- K-Means Clustering
- Warning: High Dimensions are Scary
- Extensions to KNN
- Exercises

- The Perceptron
- Bio-inspired Learning
- Error-Driven Updating: The Perceptron Algorithm
- Geometric Intrepretation
- Interpreting Perceptron Weights
- Perceptron Convergence and Linear Separability
- Improved Generalization: Voting and Averaging
- Limitations of the Perceptron
- Exercises

- Practical Issues
- The Importance of Good Features
- Irrelevant and Redundant Features
- Feature Pruning and Normalization
- Combinatorial Feature Explosion
- Evaluating Model Performance
- Cross Validation
- Hypothesis Testing and Statistical Significance
- Debugging Learning Algorithms
- Exercises

- Beyond Binary Classification
- Learning with Imbalanced Data
- Multiclass Classification
- Ranking
- Collective Classification
- Exercises

- Linear Models
- The Optimization Framework for Linear Models
- Convex Surrogate Loss Functions
- Weight Regularization
- Optimization with Gradient Descent
- From Gradients to Subgradients
- Closed-form Optimization for Squared Loss
- Support Vector Machines
- Exercises

- Probabilistic Modeling
- Classification by Density Estimation
- Statistical Estimation
- Naive Bayes Models
- Prediction
- Generative Stories
- Conditional Models
- Regularization via Priors
- Exercises

- Neural Networks
- Bio-inspired Multi-Layer Networks
- The Back-propagation Algorithm
- Initialization and Convergence of Neural Networks
- Beyond Two Layers
- Breadth versus Depth
- Basis Functions
- Exercises

- Kernel Methods
- From Feature Combinations to Kernels
- Kernelized Perceptron
- Kernelized K-means
- What Makes a Kernel
- Support Vector Machines
- Understanding Support Vector Machines
- Exercises

- Learning Theory
- The Role of Theory
- Induction is Impossible
- Probably Approximately Correct Learning
- PAC Learning of Conjunctions
- Occam's Razor: Simple Solutions Generalize
- Complexity of Infinite Hypothesis Spaces
- Learning with Noise
- Agnostic Learning
- Error versus Regret
- Exercises

- Ensemble Methods
- Voting Multiple Classifiers
- Boosting Weak Learners
- Random Ensembles
- Exercises

- Efficient Learning
- What Does it Mean to be Fast?
- Stochastic Optimization
- Sparse Regularization
- Feature Hashing
- Exercises

- Unsupervised Learning
- K-Means Clustering, Revisited
- Linear Dimensionality Reduction
- Manifolds and Graphs
- Non-linear Dimensionality Reduction
- Non-linear Clustering: Spectral Methods
- Exercises

- Expectation Maximization
- Clustering with a Mixture of Gaussians
- The Expectation Maximization Framework
- EM versus Gradient Descent
- Dimensionality Reduction with Probabilistic PCA
- Exercises

- Semi-Supervised Learning
- EM for Semi-Supervised Learning
- Graph-based Semi-Supervised Learning
- Loss-based Semi-Supervised Learning
- Active Learning
- Dangers of Semi-Supervised Learing
- Exercises

- Graphical Models
- Exercises

- Online Learning
- Online Learning Framework
- Learning with Features
- Passive Agressive Learning
- Learning with Lots of Irrelevant Features
- Exercises

- Structured Learning Tasks
- Exercises

- Bayesian Learning
- Exercises

- Code and Datasets
- Notation
- Bibliography
- Index

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