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

Organizer: Doug Chang

Instructors: Dr. Michael Bowles & Dr. Patricia Hoffman

Our objective for this class was to enable students 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 their particular problems. The first five weeks covered basic techniques in ML at sufficient depth that allowed the students to apply the techniques to real problems. The second five weeks culminated in the students giving presentations on papers they read.

R was the lingua franca for looking at homework problems, discussing them and comparing different solution approaches.

Here is what was on the web site for that class:

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:**

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