1. Godfather to the Singularity
Machine Learning
Godfather to the Singularity

2. Traditional programming
Machine learning
Computer
Data
Output
Program
Computer
Data
Program
Output

3. Machine Learning Applications

4. Visual Search, Waterfalls
User’s Query:
System’s Response:
User Feedback:
Yes
Yes
Yes
NO!

5. Example: Boundary Detection
Is this a boundary?

6. Learning a classifier
Given some set of features with corresponding labels, learn a function to predict the labels from the features x o x2 x1

7. Sample Applications Web search Computational biology Finance
E-commerce
Space exploration
Robotics
Information extraction
Social networks
Debugging
[Your favorite area]

8. Other Applications of ML
The Google search engine uses numerous machine learning techniques
Spelling corrector: “spehl korector”, “phonitick spewling”, “Brytney Spears”, “Brithney Spears”, …
Grouping together top news stories from numerous sources (news.google.com)
Analyzing data from over 3 billion web pages to improve search results
Analyzing which search results are most often followed, i.e. which results are most relevant

9. Other Applications of ML (cont’d)
ALVINN, developed at CMU, drives autonomously on highways at 70 mph
Sensor input only a single, forward-facing camera

10. Other Applications of ML (cont’d)
SpamAssassin for filtering spam
Data mining programs for:
Analyzing credit card transactions for anomalies
Analyzing medical records to automate diagnoses
Intrusion detection for computer security
Speech recognition, face recognition
Biological sequence analysis
Each application has its own representation for features, learning algorithm, hypothesis type, etc.

11. How Do We Learn? Human Machine Memorize
k-Nearest Neighbors, Case-based learning
Observe someone else, then repeat
Supervised Learning, Learning by Demonstration
Keep trying until it works (riding a bike)
Reinforcement Learning
20 Questions
Decision Tree
Pattern matching (faces, voices, languages)
Pattern Recognition
Guess that current trend will continue (stock market, real estate prices)
Regression

12. General Inductive Learning (Scientific Method)
Hypothesis
Induction, generalization
Actions, guesses
Refinement
Feedback, more observations
Observations

13. What is Machine Learning?
Building machines that automatically learn from experience
Important research goal of artificial intelligence
Applications:
Data mining programs that learn to detect fraudulent credit card transactions
Programs that learn to filter spam
Autonomous vehicles that learn to drive on public highways

14. Why use Machine Learning?
We cannot write the program ourselves
We don’t have the expertise (circuit design)
We cannot explain how (speech recognition)
Problem changes over time (packet routing)
Need customized solutions (spam filtering)

15. Machine Learning
Optimize a criterion (reach a goal) using example data or past experience
Infer or generalize to new situations
Statistics: inference from a (small) sample
Probability: distributions and models
Computer Science:
Algorithms: solve the optimization problem efficiently
Data structures: represent the learned model

16. Slide: Erik Sudderth

17. Technologies Supervised learning Unsupervised learning
Decision tree induction
Inductive logic programming
Instance-based learning
Bayesian learning
Neural networks
Support vector machines (SVM)
Model ensembles
Learning theory
Unsupervised learning
Clustering
Dimensionality reduction

18. Regression Methods k-Nearest Neighbors Support Vector Machines
Neural Networks
Bayes Estimator

19. Unsupervised Learning
No labels or feedback
Learn trends, patterns
Applications
Customer segmentation: e.g., targeted mailings
Image compression
Image segmentation: find objects
This course
k-means and EM clustering
Hierarchical clustering

20. Reinforcement Learning
Learn a policy: sequence of actions
Delayed reward
Applications
Game playing
Balancing a pole
Solving a maze
This course
Temporal difference learning

21. Hypothesis Type: Artificial Neural Network
Designed to simulate brains
“Neurons” (processing units) communicate via connections, each with a numeric weight
Learning comes from adjusting the weights

22. Perceptron (Simple Neural Net)
A single layer feed-forward network consists of one or more output neurons, each of which is connected with a weighting factor wij to all of the inputs xi. b xi b

23. Machine Learning vs. Expert Systems
ES: Expertise extraction tedious; ML: Automatic
ES: Rules might not incorporate intuition, which might mask true reasons for answer
E.g. in medicine, the reasons given for diagnosis x might not be the objectively correct ones, and the expert might be unconsciously picking up on other info
ML: More “objective”

24. Machine Learning vs. Expert Systems (cont’d)
ES: Expertise might not be comprehensive, e.g. physician might not have seen some types of cases
ML: Automatic, objective, and data-driven
Though it is only as good as the available data