Case Study I: Estimating Click Probabilities [+] Expand All[+]
Learning task I: Predicting click probabilities
X -> [0,1], where X is webpage text, query keywords, features of user, etc.
- Linear Model
- Online Learning
- Basic regularization (L2)
- Challenge: High dimensional feature space
- Challenge: Changing dimensionality of the feature space
- Advanced approach: Sketching (Bloom filter, Count-Min sketch, hash kernels)
Learning task II: Personalization
- Multitask learning
- Hashing kernel
- 1. Jan 8: Intro. Linear model for estimating click
probabilities, logistic regression, gradient descent.
[Intro slides] [LR slides] [LR annotated slides]
- 2. Jan 10: Online learning, Perceptron, kernel trick, kernelized Perceptron.
[Regularization,Perceptron slides] [Regularization,Perceptron annotated slides]
- 3. Jan 15: Kernel trick continued, stochastic gradient
[Kernelized perceptron, SGD slides] [Kernelized perceptron, SGD annotated slides]
- 4. Jan 17: SGD continued, hashing and sketching.
[ SGD continued, hashing and sketching slides]