Difference between revisions of "Class meeting for 10-605 in Fall 2016 Overview"
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=== Things to remember === | === 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 | ||
Revision as of 13:20, 14 October 2015
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Spring_2015.
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
