ML - Assignment 1

Assignment 1
Applying Machine Learning

Due: Tuesday March 3rd

Machine Learning
2009-I

Everybody in the course has an assigned task (see table below).
The task may be accomplished individually or in group.
On March 3rd, each group must bring a presentation (maximum 6 slides) that:

describes the applied technique and
presents the exercise results.

Task	Assigned to
1. To get IRIS data set http://archive.ics.uci.edu/ml/datasets/Iris	Everybody
2. To describe the data set Origin, attributes, classes "Scatter plot" 2D visualization (PCA or MDS)	JAIME EDUARDO BELTRAN PARDO JOHN MORENO
3. Decision trees: train a decision tree describe the obtained model evaluate its performance	JOSE LUIS MORALES ANDRES CASTILLO
4. Neural network: design an appropriate network to solve the problem train it evaluate its performance	WILFREDY SANTAMARIA RUIZ PAULO CESAR ROJAS GUILLEN
5. Naïve Bayes: train a Naive-Bayes classifier describe the obtained model evaluate its performance	TATIANA SUAREZ FONTANILLA ANDRES LEONARDO CUBILLOS RODRIGUEZ
6. Linear Regression: propose a linear model to solve the problem (1 class against the others, multi-class) evaluate its performance	JULIAN DAVID VARGAS ALVAREZ RODOLFO ALEXANDER TORRES PORTILLA JOHAN LEITHON
7. k-nearest neighbors train a k-nn classifier describe the obtained model evaluate its performance	OMAR GUILLERMO ERAZO CRUZ ANYELA MILENA CHAVARRO MUÑOZ
8. Support Vector Machine: train a SVM classifier describe the obtained model evaluate its performance	ANDRES YESID RAMIREZ AYA RAUL ERNESTO TORRES CARVAJAL
10. K-means: cluster the data using K-means describe the obtained clustering visualize the results	LUIS HERNAN OCHOA GUTIERREZ ROBINSON ANDRES JAQUE PIRABAN
11. Hierarchical Clustering: cluster the data using hierarchical clustering describe the obtained clustering isualize the results	EDUARDO JAVIER ORTEGA URREGO ALVARO ENRIQUE UZAHETA BERDUGO