Books references

This is roughly the elements needed for each slide

First Part - Linear Models

01 Introduction

• Mitchel - Chapter 1 : Introduction
  • 1.1 : Well-posed learning problems
  • 1.2 : Designing a learning system
  • 1.3 : Perspectives and issues in machine learning
• Norvig - Chapter 19 : Learning from examples (optional)
  • 19.1 : Forms of learning
  • 19.2 : Supervised learning
• Mitchel - Chapter 2 : Concept learning and general to specific ordering
  • 2.1 : A concept learning task
  • 2.3 : Concept learning as a search
  • 2.5 : Version spaces and the candidate-elimination algorithm
  • 2.6 : Remarks on version spaces and candidate-elimination (optional)
  • 2.7 : Inductive bias

02 Classification and evaluation

• Mitchel - Chapter 5 : Evaluating hypotesis
  • 5.1 : Motivation
  • 5.2 : Estimating hypotesis accuracy
  • 5.3 : Basics of Sampling theory
    • 5.3.4 : Estimators, bias and variance
  • 5.5 : Difference in error of two hypotesis
• Google developers - Classification:
  • true vs false and positive vs negative
  • Accuracy
  • Precision and recall
  • Roc curve and UAC

03 Decision trees

• Mitchel - Chapter 3 : Decision trees
  • 3.1 : Introduction
• Norvig - Chapter 19 : Learning Decision trees (optional)
  • 19.3.0 : Learning Decision trees
• Mitchel - Chapter 3
  • 3.2 : Decision Tree Representation
  • 3.3 : Appropriate problems for decision tree learning
• Norvig - Chapter 19 (optional)
  • 19.3.1 : Expressiveness of decision trees
  • 19.3.2 : Learning decision trees from examples
  • 19.3.3 : Choosing attributes to test
• Bishop - Chapter 1 (optional):
  • 1.6 : Information theory
• Mitchel - Chapter 3
  • 3.4 : Basic decision tree learning algorithm
  • 3.5 : Hypotesis space search in decision tree learning
  • 3.6 : Inductive bias in decision tree learning
  • 3.7 : Issues in decision learning
• Norvig - Chapter 19
  • 19.3.4 : Generalization and overfitting

04 Probability and Bayes networks

• Bishop - Chapter 1 : Introduction:
  • 1.2 : Probability theory
• Norvig chapter 12 : Quantifying uncertainty:
  • 12.1 : Acting under uncertainty
  • 12.2 : Basic probability notation
  • 12.3 : Inference using full joint distribution
  • 12.4 : Indipendence
  • 12.5 : Bayes' rule and its use
  • 12.6 : Naive Bayes' models
• Norvig - Chapter 13 : Probabilistic reasoning (optional)
  • 13.1 Representing knowlege in an uncertain domain
  • 13.2 The semantics of bayesan networks
  • 13.3 Exact inference in bayesan networks

05 Bayesian Learning

• Mitchel chapter 6 :

06 Probabilistic models for classification

• Bishop - Chapter 1 : Introduction

  • 1.5 : Decision theory
    • 1.5.1 : Minimizing the misclassifcation rate
    • 1.5.2 : Minimizing the expected loss
    • 1.5.3 : The Reject option
    • 1.5.4 : Inference and decision

• Bishop chapter 4:

  • 4.2 : Probabilistic generative models
  • 4.3 : Probabilsitic discriminative models:

07 Linear models for classification

• Bishop chapter 4:
  • 4.1 : Discriminant functions
• Mitchel chapter 4:
  • 4.1 : Introduction
  • 4.2 : Neural networks representations
  • 4.3 : Appropriate problems for neural network learning
  • 4.4 : Perceptrons
• Bishop chapter 7:
  • 7.1 : Maximum margin classifiers

08 Linear models for regression

• Bishop chapter 3:
  • 3.1 : Linear basis function models

09 Kernel Models

• Mitchel chapter 6
• Bishop chapter 7:
  • 7.1 Maximum margin classifiers