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Writing Smart Programs
Anoop Thomas Mathew
twitter: atmb4u
Profoundis Inc.
History
then:
low level → high level
now:
code driven → data driven
What is Smart
everyone makes mistakes
○ looking at past to avoid future mistakes
everyone misses what comes next
○ predicting what to expect
clusters of items do exist
○ automatically group things based on similarity
Jargon Buster
Dataset
Data Cleaning
Dimension
Model
Training
Parameters
Dimensionality Reduction
Accuracy
Overfitting
Underfitting
Testing
Domain
Parameter
Optimization
Heuristic / Statistical / Machine Learning
- “know parameters well”
Eg: stock market prediction disaster; no. of lawyers vs. no. of suicides
Supervised vs Unsupervised
we know what we want
vs.
find what’s interesting
NB: training data, accuracy, semi-supervised
Classification / Regression / Clustering
★ rain prediction
★ digit recognition
★ customer segmentation
★ time-series prediction
★ spam filtering
Algorithm Examples
★ K-means
★ SVR
★ SVC
★ Naive Bayes
★ Random Forest Decision Tree
Writing Smarter Applications with Machine Learning
The ML Process
plan → collect → execute → test
time: 50% 30% 5-10% 15-20%
DEMO 1
SHOPPING PREDICT
(https://github.com/atmb4u/smarter-apps-2016)
DEMO 2
Support Vector Machine
(https://github.com/atmb4u/smarter-apps-2016)
Dummy Tasks
★ user auto-login redirect
★ predict if a user will convert to paid
Thank You
Follow me on twitter:@atmb4u

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