# Hacker Dojo Machine Learning Class 101 & 102 Beginning Applied Learning

Organizer: Doug Chang

Instructors: Dr. Michael Bowles & Dr. Patricia Hoffman

We've put together three sequences of classes. Our objective for the second sequence is to enable you to read the current literature, to implement algorithms based on what you read and to extend or modify methods in the current machine learning oeuvre in order to suit the needs of your particular problems. The first sequence will cover basic techniques in ML at sufficient depth that you will be able to immediately apply the techniques you've learned to real problems. The first session (Machine Learning 101) in this sequence covered basic techniques in ML at sufficient depth that you will be able to immediately apply the techniques you've learned to real problems. This second five week session (Machine Learning 102) will culminate in the students giving presentations on papers they have read.

We're going to use R as our lingua franca for looking at homework problems, discussing them and comparing different solution approaches. Load R onto your laptop or desk computer before you come to the first class. http://cran.r-project.org/ We will include some descriptive material on using R in the first two lectures in order to get everyone up to speed on it. References for R are here: References for R Comment on these references here: Reference for R Comments More R references

**General Calendar for the Year:**

**Fall 2010: **Basic Machine Learning

Book: Introduction to Data Mining by Pang-Ning Tan et al

**Winter 2010**: Advanced Machine Learning

Books: Professor Andrew Ng's lecture notes from CS229 and Elements of Statistical Learning, Hastie et al

**Spring 2011:** Extended Machine Learning Project (Competition)

**Complete Outline for the first Course (Fall 2010): **

** **Machine Learning 101

Lectures are in the Lectures Folder

Homeworks are in the Homework Folder

DataFiles

There are more Machine Learning References on my web site http://patriciahoffmanphd.com/

**General Sequence of Classes:**

Beginning Applied Machine Learning

Text: "Introduction to Data Mining", by Pang-Ning Tan, Michael Steinbach and Vipin Kumar

**Machine Learning 101: ** Learn about ML algorithms and implement them in r

**Machine Learning 102: **Enable you to read and implement algorithms from current papers

Modern Applied Machine Learning

Text: "The Elements of Statistical Learning - Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

**Machine Learning 201: ** Advanced Regression Techniques, Generalized Linear Models, and Generalized Additive Models

**Machine Learning 202: **Collaborative Filtering, Bayesian Belief Networks, and Advanced Trees

Advanced Topics

**Machine Learning 300 series: **

Data Mining Social Networks

Text Mining

Recommender Methods

Big Data

** **

Extended Machine Learning Project (Competition)

**Machine Learning 400:**

## Comments (0)

You don't have permission to comment on this page.