Difference between revisions of "Class meeting for 10-605 in Fall 2016 Overview"
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| − | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in | + | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2016]]. |
=== Slides === | === Slides === | ||
Revision as of 14:18, 1 August 2016
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall 2016.
Slides
Homework
- Before the next class: watch My overview lecture from 10-601 (lecture 1, and a little of lecture 2) if you need it.
Readings for the Class
- The Unreasonable Effectiveness of Data - Halevy, Pereira, Norvig
Also discussed
- William W. Cohen (1993): Efficient pruning methods for separate-and-conquer rule learning systems in IJCAI 1993: 988-994
- William W. Cohen (1995): Fast effective rule induction in ICML 1995: 115-123.
- Scaling to very very large corpora for natural language disambiguation, Banko & Brill, ACL 2001
Things to remember
- Why use big data?
- Simple learning methods with large data sets can outperform complex learners with smaller datasets
- The ordering of learning methods, best-to-worst, can be different for small datasets than from large datasets
- The best way to improve performance for a learning system is often to collect more data
- Large datasets often imply large classifiers
- Asymptotic analysis
- It measures number of operations as function of problem size
- Different operations (eg disk seeking, scanning, memory access) can have very very different costs
- Disk access is cheapest when you scan sequentially
