Intro to Machine Learning - Supervised Learning

Intro to Machine - Supervised Learning

Instructor: Dr. Michael Bowles

This is an intense, one-day short-course aimed at programmers wanting to learn machine learning - supervised learning algorithms. The course takes a very hands-on, run-code approach.

9:30 am Registration
10:00 am - 5:00 pm Class (w. break for lunch - delivered)

Class will be delivered by webcast for those want to attend remotely. To get the webcast instructions you must sign up on eventbrite 12 hours prior to start of class.

Class Outline:

1. Review of R programming

2. Description of Supervised Learning Problem

3. Algorightms:
   k-Nearest Neighbors
   Linear Regression for Regression and Classification
   Regularized Regression (Lasso, Elasticnet, LARS, Glmnet)
   Support Vector Machine SVM
   Binary Trees for Regression and Classification
   Ensemble Methods - Gradient Boosting, Random Forests

Who Should Attend:
The class is aimed at computer programmers, computer scientists and software engineers who want to gain a working knowledge of modern machine learning and the types of problems that it can solve. Class sessions will rely heavily on code examples in R statistical programming language. Participants aren't expected to have any prior experience with R. An introduction to R will be included in the material. The material will cover an introduction to R statistical programming language, unsupervised learning (clustering) and supervised learning (predicting classification and regression – also called "predictive analytics").

Payment
-pre-pay by credit card http://intromachinelearningpart2.eventbrite.com
($225 for registration 5 days ahead of event, $275 otherwise)

-or bring cash or check.