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Intro to Machine
Learning
Corey Chivers, PhD
Senior Data Scientist
math
We’ll focus on
building intuitions
We're not trying to
learn about data,
we're trying to learn
about processes, or
phenomena
in the world.
Learning is
generalization
Not memorization
ML takes some biological
inspiration,
but machines are not
biology
Supervised Learning
Cat
Cat
Cat
Dog
Dog
Doge
Supervised Learning
• Learning mapping between examples and labels
0.7 Dog
0.25 Cat
0.05 …
Unsupervised Learning
Unsupervised Learning
Learning structure from unlabeled examples
• Topology
• Relatedness
• Motifs
NBA Players
http://www....
Reinforcement Learning
Reinforcement Learning
Learning to take actions to maximize reward
• Agents
• Games
• Policies
Google’s
Alpha GO
http://ww...
How do we build the
magic box?
• Data come in all shapes and sizes
o Text
o images
o audio
o video
o graphs (aka networks)
o gene sequences
o gravitation...
Typically, we need a vector representation
(aka a bunch o’ numbers)
Features
(not bugs)
Deep neural nets learn hierarchical levels of representation
Features
(not bugs)
http://www.datarobot.com/blog/a-primer-on...
Models
In order to find (approximate) the mapping between inputs
and outputs, we need a model
All models are
wrong, but
some are
useful.
- George Box
Fitting
Finding the highest mountain peak
Maximizing an information measure
or
Minimizing a loss function
What are the bes...
Fitting
Finding the highest mountain peak
Maximizing an information measure
or
Minimizing a loss function
What are the bes...
Learning is
generalization
Not memorization
Avoiding Over-Fitting
- Regularization
- Early stopping
- Dropout
- Bootstrapping
- Bagging
- Boosting
Many Methods, inclu...
Summary
• We are trying to learn about the world, not about the
data
• ML is about finding mappings between inputs and
out...
Data Science @
• Develop data products and predictive applications
• Apply cutting edge machine learning and computational...
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Intro to Machine Learning

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Presented at DataPhilly, February 19, 2016

Published in: Engineering
  • Very good, simple explanation of a complex domain. Thanks
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Intro to Machine Learning

  1. 1. Intro to Machine Learning Corey Chivers, PhD Senior Data Scientist
  2. 2. math We’ll focus on building intuitions
  3. 3. We're not trying to learn about data, we're trying to learn about processes, or phenomena in the world.
  4. 4. Learning is generalization Not memorization
  5. 5. ML takes some biological inspiration, but machines are not biology
  6. 6. Supervised Learning Cat Cat Cat Dog Dog Doge
  7. 7. Supervised Learning • Learning mapping between examples and labels 0.7 Dog 0.25 Cat 0.05 …
  8. 8. Unsupervised Learning
  9. 9. Unsupervised Learning Learning structure from unlabeled examples • Topology • Relatedness • Motifs NBA Players http://www.sloansportsconference.com/wp-content/uploads/2012/03/Alagappan-Muthu-EOSMarch2012PPT.pdf
  10. 10. Reinforcement Learning
  11. 11. Reinforcement Learning Learning to take actions to maximize reward • Agents • Games • Policies Google’s Alpha GO http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html
  12. 12. How do we build the magic box?
  13. 13. • Data come in all shapes and sizes o Text o images o audio o video o graphs (aka networks) o gene sequences o gravitational waves • In order for a machine to learn from these data, we first need to represent them. Features (not bugs)
  14. 14. Typically, we need a vector representation (aka a bunch o’ numbers) Features (not bugs)
  15. 15. Deep neural nets learn hierarchical levels of representation Features (not bugs) http://www.datarobot.com/blog/a-primer-on-deep-learning/
  16. 16. Models In order to find (approximate) the mapping between inputs and outputs, we need a model
  17. 17. All models are wrong, but some are useful. - George Box
  18. 18. Fitting Finding the highest mountain peak Maximizing an information measure or Minimizing a loss function What are the best parameters?
  19. 19. Fitting Finding the highest mountain peak Maximizing an information measure or Minimizing a loss function What are the best parameters?
  20. 20. Learning is generalization Not memorization
  21. 21. Avoiding Over-Fitting - Regularization - Early stopping - Dropout - Bootstrapping - Bagging - Boosting Many Methods, including http://mathbabe.org
  22. 22. Summary • We are trying to learn about the world, not about the data • ML is about finding mappings between inputs and outputs that generalize to new inputs • This is done by representing data as features, defining a model and using optimization to find the best parameters using data.
  23. 23. Data Science @ • Develop data products and predictive applications • Apply cutting edge machine learning and computational statistics. • Collaborate with top medical professionals • Revolutionize Health care delivery Contact: corey.chivers@uphs.upenn.edu @cjbayesian

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