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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 Mondays 19-20:40 CB01
Wednesdays 21-22:40 CB02


Extra-Class Office Hours (Prof. Anderson Rocha): Every Tuesday from 18:00 to 19:15, Office #79, IC/Unicamp. Extra-Class Office Hours (Samuel Fadel): Every Thursday from 17:30 to 19:30, Room #322, IC/Unicamp.

Posts:

16/May/2018 Assignment #04 is now available.
23/April/2018 Assignment #03 is now available.
02/April/2018 Assignment #02 is now available.
19/March/2018 As we have discussed last class, we WILL NOT have classes this week (March 19th and March 21st). TA Samuel Fadel will be in the classroom taking questions.
13/March/2018 Assignment #01 is now available.
12/March/2018 Lectures slides (4,5,6) are available.
06/March/2018 Lectures slides (1,2,3) are available.
22/Jan/2018 Class description, rules and syllabus are already available.


Class Materials

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

(PDF)

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

Reading: IAAM, Chapter #1/2/3; PRML, Chapter #1		 3.4 MB

(PDF)

Lecture Complementary Slides Introduction to Machine Learning, problems, data, tools.

Reading: IAAM, Chapter #1/2/3; PRML, Chapter #1		 38 MB

(PDF)

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Lecture Slides Linear Regression, Cost Function, Gradient Descent, Generalization of GD, Model Complexity, Overfitting/Overfitting, Multi-variate Regression.

Reading: Elements, Chapter #3; PRML, Chapter #3		 15 MB

(PDF)

Lecture Slides Logistic Regression, hypothesis representation, decision boundary, cost function, simplified cost function, GD, multiclass classification.

Reading: Elements, Chapter #4/5; PRML, Chapter #3/4		 7 MB

(PDF)

Lecture Slides Regularization, bias/variance.

Reading: Elements, Chapter #5; PRML, Chapter #3		 4 MB

(PDF)

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Lecture Slides Perceptrons, neural networks.

Reading: IAAM, Chapter #7; PRML, Chapter #5		 11.7 MB

(PDF)

Lecture Slides Neural networks (continued).

Reading: IAAM, Chapter #7; PRML, Chapter #5		 12.7 MB

(PDF)

Lecture Slides Deep Learning (Part #1).

Reading: Ian Goodfellow's Book - Chapter Intro and CNNs		 7.6 MB

(PDF)

Lecture Slides Deep Learning (Part #2).

Reading: Ian Goodfellow's Book - Chapter Intro and CNNs		 5.4 MB

(PDF)

Lecture Slides Deep Learning (Part #3).

Reading: Ian Goodfellow's Book - Chapter Intro and CNNs		 4.9 MB

(PDF)

Lecture Slides Deep Learning (Part #4).

Reading: Ian Goodfellow's Book - Chapter Intro and CNNs		 6.4 MB

(PDF)

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Lecture Slides Unsupervised Learning - Clustering (Part #1).

Reading: Elements Chapter 12, PRML Chapter 9, IAAM Chapter 12		 26.2 MB

(PDF)

Lecture Slides Unsupervised Learning - Clustering (Part #2).

Reading: Elements Chapter 12, PRML Chapter 9, IAAM Chapter 12		 9 MB

(PDF)

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Lecture Slides Dimensionality Reduction (Part #1).

Reading: PRML Chapter 12, Elements Chapter 4, A Tutorial on PCA, Jonathon Shlens (PDF)		 1.2 MB

(PDF)

Lecture Slides Dimensionality Reduction (Part #2).

Reading: PRML Chapter 12, Elements Chapter 4, A Tutorial on PCA by Jonathon Shlens (PDF)		 3.1 MB

(PDF)

Lecture Slides Dimensionality Reduction (t-SNE).

Reading: Visualizing Data using t-SNE (PDF)		 0.6 MB

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Lecture Slides Support Vector Machines (Part #1).

Reading: 		1.9 MB

(PDF)

Lecture Slides Support Vector Machines (Part #2).

Reading: 		1.7 MB

(PDF)

Lecture Slides Support Vector Machines (Part #3).

Reading: 		2.0 MB

(PDF)

Lecture Slides Classification Trees.

Reading: Mitchell, Chapter 3		 1.7 MB

(PDF)

Class Material #01

Subjects

  1. Introduction to ML
  2. Supervised Learning vs Unsupervised Learning vs Semi-Supervised Learning
  3. Linear 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)

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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 #08

Subjects

  1. Support Vector Machines (I)
 412 KB

(PDF)

Class Material #09

Subjects

  1. Support Vector Machines (II)
 692 KB

(PDF)

Class Material #10

Subjects

  1. Support Vector Machines (III)
 360 KB

(PDF)

Class Material #06

Subjects

  1. Decision tree learning
 5.9 MB

(PDF)

Class Material #13

Subjects

  1. Naive Bayes
 1.8 MB

(PDF)

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Class Material #07

Subjects

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

(PDF)

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Class Material #06

Subjects

  1. Decision tree learning
 5.9 MB

(PDF)

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Class Material #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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Individual Practical Assignments

Practical Assignment #4 Description | Dataset

Practical Assignment #3 Description | Dataset

Practical Assignment #2 Description | Dataset Train | Dataset Test | Recommended Reading #1 | Recommended Reading #2

Practical Assignment #1 Description | Dataset

Reports. Use this model for the assignment reports.

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