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Machine Learning Short Course - Intro to Recommender Systems

Setup Time:
None minutes
Start:
Saturday, September 22, 2012 at 09:00am
End:
Saturday, September 22, 2012 at 04:00pm
Teardown Time:
None minutes
Type:
Meetup
Estimated size:
40
Contact:
Mike Bowles
Fee:
200
Rooms:
140b

Details:
How do companies like Amazon, Netflix and eBay decide what products and links to suggest? Recommender systems are algorithms that incorporate past user behavoir, query strings, product descriptions and product reviews to produce highly targeted recommendations. In this short course, you'll learn about the techniques and algorithms available for these sorts of problems and how your problem statement might affect the choice of algorithms.

This class is aimed at computer scientists interested to learn about recommender systems. No previous experience with machine learning is required. You'll see code examples for the various algorithms and then use this code to build your own recommender systems in a variety of different problems. We'll use R programming language for running algorithms and will provide a brief introduction to R in order to get everyone up to speed.

Outline
1. Introduction to R programming Language
2. Background on Recommender Systems
3. Content based recommendations
4. Transactions based recommendations
User based
Item based
Association Rule approach
Matrix-based algo
5. Scaling up

Notes:


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