1. Supervised learning Given training examples of inputs and corresponding outputs, produce the “correct” outputs for new inputs Two main scenarios: –Classification: outputs are discrete variables (category labels). Learn a decision boundary that separates one class from the other –Regression: also known as “curve fitting” or “function approximation.” Learn a continuous input-output mapping from examples (possibly noisy)

2. Regression: example 1 Suppose we want to predict gas mileage of a car based on some characteristics: number of cylinders or doors, weight, horsepower, year etc. Source: G. Shakhnarovich

3. Regression: example 2 Training set: faces (represented as vectors of distances between keypoints) together with experimentally obtained attractiveness rankings Learn: function to reproduce attractiveness ranking based on training inputs and outputs Vector of distances v Attractiveness score f(v) T. Leyvand, D. Cohen-Or, G. Dror, and D. Lischinski, Data-driven enhancement of facial attractiveness, SIGGRAPH 2008

4. Structured Prediction Image Word Source: B. Taskar

5. Structured Prediction Sentence Parse tree Source: B. Taskar

6. Structured Prediction Sentence in two languages Word alignment Source: B. Taskar

7. Structured Prediction Amino-acid sequence Bond structure Source: B. Taskar

8. Structured Prediction Many image-based inference tasks can be thought of as “structured prediction” Data association problem Source: D. Ramanan model

9. Unsupervised Learning Given only unlabeled data as input, learn some sort of structure The objective is often more vague or subjective than in supervised learning. This is more of an exploratory/descriptive data analysis

10. Unsupervised Learning Clustering –Discover groups of “similar” data points

11. Unsupervised Learning Quantization –Map a continuous input to a discrete (more compact) output 1 2 3

12. Unsupervised Learning Density estimation –Find a function that approximates the probability density of the data (i.e., value of the function is high for “typical” points and low for “atypical” points) –Can be used for anomaly detection

13. Active learning The learning algorithm can choose its own training examples, or ask a “teacher” for an answer on selected inputs S. Vijayanarasimhan and K. Grauman, “Cost-Sensitive Active Visual Category Learning,” 2009

14. Course themes Formal representations High-level reasoning vs. low-level statistical modeling Combinatorial vs. statistical methods Computational complexity barriers “Neat” vs. “scruffy” approaches Building blocks vs. real-world systems

15. What wasn’t covered? Continuous environments Multi-agent environments Sensing and acting in the real world Natural language understanding, machine translation Knowledge engineering