Location: Tigard, OR Venue: Embassy Suites Portland - Washington Square Date: September 17, 2014 Time: 9:00 a.m. - 12:00 p.m.
Machine learning techniques are often used for analysis and decision-making tasks such as forecasting, pattern recognition, and data mining. However, implementing and comparing machine learning techniques to choose the best approach can be challenging.
In this free seminar, you will learn about several machine learning techniques available in MATLAB and how to quickly explore your data, evaluate machine learning algorithms, compare the results, and apply the best technique to your problem.
Highlights include unsupervised and supervised learning techniques such as:
K-means and other clustering tools
Decision trees and ensemble learning
Naïve Bayes Classification
Linear, logistic and nonlinear regression
Registration and Continental Breakfast
Introduction to MATLAB for Machine Learning
Unsupervised Learning: Clustering and Data Mining
This section uses a human activity recognition example to demonstrate finding patterns in unlabeled data using various clustering techniques.
Supervised Learning: Regression and Classification
Continuing with the previous example, this section demonstrates applying supervised learning techniques to train, test, refine and compare multiple classifiers.
Q&A and Next Steps
Conclusion of the Seminar