Books & Videos

Table of Contents

  1. Chapter 1 Probably Approximately Correct Software

    1. Writing Software Right

    2. Writing the Right Software

    3. The Plan for the Book

  2. Chapter 2 A Quick Introduction to Machine Learning

    1. What Is Machine Learning?

    2. Supervised Learning

    3. Unsupervised Learning

    4. Reinforcement Learning

    5. What Can Machine Learning Accomplish?

    6. Mathematical Notation Used Throughout the Book

    7. Conclusion

  3. Chapter 3 K-Nearest Neighbors

    1. How Do You Determine Whether You Want to Buy a House?

    2. How Valuable Is That House?

    3. Hedonic Regression

    4. What Is a Neighborhood?

    5. K-Nearest Neighbors

    6. Mr. K’s Nearest Neighborhood

    7. Distances

    8. Curse of Dimensionality

    9. How Do We Pick K?

    10. Valuing Houses in Seattle

    11. Conclusion

  4. Chapter 4 Naive Bayesian Classification

    1. Using Bayes’ Theorem to Find Fraudulent Orders

    2. Conditional Probabilities

    3. Probability Symbols

    4. Inverse Conditional Probability (aka Bayes’ Theorem)

    5. Naive Bayesian Classifier

    6. Naiveté in Bayesian Reasoning

    7. Pseudocount

    8. Spam Filter

    9. Conclusion

  5. Chapter 5 Decision Trees and Random Forests

    1. The Nuances of Mushrooms

    2. Classifying Mushrooms Using a Folk Theorem

    3. Finding an Optimal Switch Point

    4. Pruning Trees

    5. Conclusion

  6. Chapter 6 Hidden Markov Models

    1. Tracking User Behavior Using State Machines

    2. Emissions/Observations of Underlying States

    3. Simplification Through the Markov Assumption

    4. Hidden Markov Model

    5. Evaluation: Forward-Backward Algorithm

    6. The Decoding Problem Through the Viterbi Algorithm

    7. The Learning Problem

    8. Part-of-Speech Tagging with the Brown Corpus

    9. Conclusion

  7. Chapter 7 Support Vector Machines

    1. Customer Happiness as a Function of What They Say

    2. The Theory Behind SVMs

    3. Sentiment Analyzer

    4. Aggregating Sentiment

    5. Mapping Sentiment to Bottom Line

    6. Conclusion

  8. Chapter 8 Neural Networks

    1. What Is a Neural Network?

    2. History of Neural Nets

    3. Boolean Logic

    4. Perceptrons

    5. How to Construct Feed-Forward Neural Nets

    6. Building Neural Networks

    7. Using a Neural Network to Classify a Language

  9. Chapter 9 Clustering

    1. Studying Data Without Any Bias

    2. User Cohorts

    3. Testing Cluster Mappings

    4. K-Means Clustering

    5. EM Clustering

    6. The Impossibility Theorem

    7. Example: Categorizing Music

    8. Conclusion

  10. Chapter 10 Improving Models and Data Extraction

    1. Debate Club

    2. Picking Better Data

    3. Feature Transformation and Matrix Factorization

    4. Ensemble Learning

    5. Conclusion

  11. Chapter 11 Putting It Together: Conclusion

    1. Machine Learning Algorithms Revisited

    2. How to Use This Information to Solve Problems

    3. What’s Next for You?