Presented at DataPhilly, February 19, 2016

1. Intro to Machine
Learning
Corey Chivers, PhD
Senior Data Scientist

2. math
We’ll focus on
building intuitions

3. We're not trying to
learn about data,
we're trying to learn
about processes, or
phenomena
in the world.

4. Learning is
generalization
Not memorization

5. ML takes some biological
inspiration,
but machines are not
biology

6. Supervised Learning
Cat
Cat
Cat
Dog
Dog
Doge

7. Supervised Learning
• Learning mapping between examples and labels
0.7 Dog
0.25 Cat
0.05 …

8. Unsupervised Learning

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. Reinforcement Learning

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. How do we build the
magic box?

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. Typically, we need a vector representation
(aka a bunch o’ numbers)
Features
(not bugs)

15. Deep neural nets learn hierarchical levels of representation
Features
(not bugs)
http://www.datarobot.com/blog/a-primer-on-deep-learning/

16. Models
In order to find (approximate) the mapping between inputs
and outputs, we need a model

17. All models are
wrong, but
some are
useful.
- George Box

18. Fitting
Finding the highest mountain peak
Maximizing an information measure
or
Minimizing a loss function
What are the best parameters?

19. Fitting
Finding the highest mountain peak
Maximizing an information measure
or
Minimizing a loss function
What are the best parameters?

20. Learning is
generalization
Not memorization

21. Avoiding Over-Fitting
- Regularization
- Early stopping
- Dropout
- Bootstrapping
- Bagging
- Boosting
Many Methods, including
http://mathbabe.org

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. 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