| 
  • If you are citizen of an European Union member nation, you may not use this service unless you are at least 16 years old.

  • You already know Dokkio is an AI-powered assistant to organize & manage your digital files & messages. Very soon, Dokkio will support Outlook as well as One Drive. Check it out today!

View
 

FrontPage

This version was saved 13 years, 5 months ago View current version     Page history
Saved by hoffman.tricia@gmail.com
on December 7, 2010 at 7:44:29 pm
 

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

 

 

1st Week  
 
22-Sep Chapter 1 & 2                                       Notes For First Week
Papers
23-Sep Chapter 3  
2nd Week  
 
29-Sep Chapter 4                                                Notes for 2nd Week

HW #1 Due

HomeworkAssignment01.doc

HW # 1  HW1.pdf

30-Sep Chapter 4   
3rd Week  
 
6-Oct Simple Regression                                 Notes for  Week 03

HW # 2 Due

Homework02.doc

Homework02.pdf

7-Oct Ridge Regression & k Nearest Neighbors
 
4rd Week  
 
13-Oct

Chapter 5                                                                 Week04

finish k Nearest Neighbors

Naive Bayes

HW #3 Due

Homework03.doc Homework03.pdf

14-Oct

Chapter 5

Support Vector Machines

 
5th Week  
 
20-Oct

Chapter 5 Week05

finish SVM

Start Ensemble Methods

HW #4 Due

Homework04.doc

Homework04.pdf

 

21-Oct

Chapter 5

Finish Ensemble Methods

 
     
  Machine Learning 102  
6th Week  
 
27-Oct

Chapter 5  Week06

Class Imbalance

HW #5 Due

Homework05.doc  Homework05.pdf

28-Oct Chapter 6  
7th Week  
 
3-Nov Chapter 8 Week07

HW #6 Due

Homework06.doc  Homework06.pdf

4-Nov Chapter 8 Cluster Analysis  
8th Week  
 
10-Nov

Papers Week08

Group 3, Group 4

Work Hard on your Presentations
11-Nov Papers  Group 1, Group 2  
13-Nov    Data Mining Camp Saturday Instructions 
9th Week  
 
17-Nov Chapter 9 Week09  HW #7 on Chapter 8  Due
18-Nov Chapter 9  
10th Week  
1-Dec Chapter 10, Week10  HW #8 on Chapter 9 Due
2-Dec Chapter 10  
 
 
 

 

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.