A Course in Machine Learning
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Description
Contents
Reviews
Language
English
ISBN
Unknown
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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