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Homepage for the Discipline MO444 and MC886


Class Materials | Support Material | Presentations | Practical Assignments


Apresentação

Professor: Anderson Rocha

Class Day Time Room
A Wednesdays 21-22:40 PB18
Fridays 19-20:40 PB18


Extra-Class Office Policy: Schedule by e-mails.

Avisos:

12/08/2015 Class description, rules and syllabus are already available.


Class Materials

Aula #0 - Presentation of the discipline. Syllabus. 121 KB

(PDF)

Introduction Class -- Introduction to Machine Learning, problems, data, tools.

Reading: IAAM, Chapter #1 e #2; PRML, Chapter #1
1.2 MB

(PDF)

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Class Material #01

Subjects

  1. Introduction to ML
  2. Supervised Learning vs Unsupervised Learning vs Semi-Supervised Learning
  3. Liner Regression
  4. Cost Function
  5. Gradient Descent
  6. Generalization of Gradient Descent
  7. Model Complexity
  8. Overfitting vs. Generalization
  9. Multi-variate Regression
  10. Normalization
  11. Polynomial Regression
  12. Normal Equations vs. Gradient Descent
  13. Logistic Regression
  14. Decision Boundaries
  15. Logistic Regression and Cost Function
  16. Logistic Regression and Multi-class extensions
  17. Regularization
  18. Regularized Linear Regression and Logistic Regression
12.3 MB

(PDF)

Class Material #02

Subjects

  1. Perceptron
  2. Effects of Dimensionality
  3. Neural Networks
  4. Cost Function
  5. Backpropagation
  6. Gradient Checking
7.8 MB

(PDF)

Class Material #03

Subjects

  1. Unsupervised Learning
  2. Clustering
  3. K-Means
  4. Hard vs. Soft Assignment
  5. Gaussian Mixture Models (GMMs)
  6. Expectation/Maximization (EM)
  7. Dimensionality Reduction
  8. PCA and LDA
  9. Multi-class LDA
1.4 MB

(PDF)

Class Material #04

Subjects

  1. Evolutionary Computing
  2. Genetic Algorithms
  3. Genetic Programming
  4. Evolutionary Programming
  5. Evolutionary Strategies
  6. Operators
  7. Problem Examples
4.2 MB

(PDF)

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Class Material #05

Subjects

  1. Data Representation vs. Data Classification
  2. Debugging an ML solution
  3. Performance Evaluation
  4. Bias vs. Variance
  5. ROC curves
  6. Bootstrapping
  7. Statistical Tests
  8. Wilcoxon Sign-Rank Test
  9. Friedman Test
  10. Post-tests
15.0 MB

(PDF)

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Class Material #06

Subjects

  1. Decision tree learning
5.9 MB

(PDF)

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Class Material #07

Subjects

  1. Sampling Theory
  2. Bagging
  3. Boosting
3.0 MB

(PDF)

Class Material #08

Subjects

  1. Support Vector Machines (I)
412 KB

(PDF)

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Class Material #09

Subjects

  1. Support Vector Machines (II)
692 KB

(PDF)

Class Material #10

Subjects

  1. Support Vector Machines (III)
360 KB

(PDF)

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Class Material #11

Subjects

  1. Random Forests (I)
1.8 MB

(PDF)

Class Material #12

Subjects

  1. Random Forests (II)
1.8 MB

(PDF)

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Class Material #13

Subjects

  1. Naive Bayes
1.8 MB

(PDF)

Class Material #15

Subjects

  1. Deep Learning
25 MB

(ZIP)

Class Material #16

Subjects

  1. Optimum-Path Forest Classifier (OPF)
6.6 MB

(PDF)

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Reports. Use this model for the assignment reports.

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