A Course in Machine Learning
Free

A Course in Machine Learning

By Hal Daumé III
Free
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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