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	PB11
	Fridays	19-20:40	PB11

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

Avisos:

15/11/2014	The fourth individual practical assignment is already available.
29/10/2014	The third individual practical assignment is already available.
08/10/2014	The second individual practical assignment is already available.
17/09/2014	The first individual practical assignment is already available.
03/09/2014	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 Introduction to ML Supervised Learning vs Unsupervised Learning vs Semi-Supervised Learning Liner Regression Cost Function Gradient Descent Generalization of Gradient Descent Model Complexity Overfitting vs. Generalization Multi-variate Regression Normalization Polynomial Regression Normal Equations vs. Gradient Descent Logistic Regression Decision Boundaries Logistic Regression and Cost Function Logistic Regression and Multi-class extensions Regularization Regularized Linear Regression and Logistic Regression

12.3 MB

(PDF)

Class Material #02 Subjects Perceptron Effects of Dimensionality Neural Networks Cost Function Backpropagation Gradient Checking

7.8 MB

(PDF)

Class Material #03 Subjects Unsupervised Learning Clustering K-Means Hard vs. Soft Assignment Gaussian Mixture Models (GMMs) Expectation/Maximization (EM) Dimensionality Reduction PCA and LDA Multi-class LDA

1.4 MB

(PDF)

Class Material #04 Subjects Evolutionary Computing Genetic Algorithms Genetic Programming Evolutionary Programming Evolutionary Strategies Operators Problem Examples

4.2 MB

(PDF)

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Class Material #05 Subjects Data Representation vs. Data Classification Debugging an ML solution Performance Evaluation Bias vs. Variance ROC curves Bootstrapping Statistical Tests Wilcoxon Sign-Rank Test Friedman Test Post-tests

15,0 MB

(PDF)

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Class Material #06 Subjects Decision tree learning

5,9 MB

(PDF)

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Class Material #07 Subjects Sampling Theory Bagging Boosting

3,0 MB

(PDF)

Class Material #08 Subjects Support Vector Machines (I)

412 KB

(PDF)

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Class Material #09 Subjects Support Vector Machines (II)

692 KB

(PDF)

Class Material #10 Subjects Support Vector Machines (III)

360 KB

(PDF)

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Class Material #11 Subjects Random Forests

1,8 MB

(PDF)

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Individual Practical Assignments

Practical Assignment #1 Definitions and datasets

Practical Assignment #2 Definitions and Readings | Datasets

Practical Assignment #3 Definitions and Readings | Datasets

Practical Assignment #4 Definitions and Readings | Datasets

Reports. Use this model for the assignment reports.

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