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Machine Learning ICS 273A

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Machine Learning ICS 273A Instructor: Max Welling
What is Expected? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Syllabus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Machine Learning according to ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Some Examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Can Computers play Humans at Chess? ,[object Object],[object Object],[object Object],[object Object],Garry Kasparov (current World Champion ) Deep Blue Deep Thought Points Ratings
2005 DARPA Challenge The Grand Challenge is an off-road robot competition devised by DARPA (Defense Advanced Research Projects Agency) to promote research in the area of autonomous vehicles. The challenge consists of building a robot capable of navigating 175 miles through desert terrain in less than 10 hours, with no human intervention.
Why is this cool/important? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Types of Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ingredients ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Supervised Learning I Example: Imagine you want to classify versus Data : 100 monkey images and 200 human images with labels what is what. where x represents the greyscale of the image pixels and y=0 means “monkey” while y=1 means “human”. Task : Here is a new image: monkey or human?
1 nearest neighbors (your first ML algorithm!) ,[object Object],[object Object],[object Object],[object Object],[object Object],query closest image Homework: write pseudo-code for the k-nearest neighbor algorithm
1NN Decision Surface decision curve
Distance Metric ,[object Object],[object Object],[object Object]
Remarks on NN methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Non-parametric Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],Homework: read book chapter 8, 8.1 & 8.2
Logistic Regression / Perceptron ,[object Object],1 dimension 2 dimensions (your second ML algorithm!)
The logit / sigmoid Determines the offset Determines the angle and the steepness.
Objective ,[object Object],[object Object]
Algorithm in detail ,[object Object],(Or better: Newton steps) Homework: Derive all these update equations for yourself.
Parametric Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Hypothesis Space ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],1 0 0 1 1
Inductive Bias ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Generalization ,[object Object],[object Object],[object Object],[object Object],Homework: Write pseudo-code for a NN regression algorithm. X*
Generalization
Generalization
Generalization which curve is best?
[object Object],Generalization
Generalization Learning is concerned with accurate prediction of future data, not accurate prediction of training data. (The single most important sentence you will see in the course)
Cross-validation ,[object Object],[object Object],[object Object],[object Object],[object Object],How do we ensure good generalization, i.e. avoid “over-fitting” on our particular data sample.

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Machine Learning ICS 273A

  • 1. Machine Learning ICS 273A Instructor: Max Welling
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7. 2005 DARPA Challenge The Grand Challenge is an off-road robot competition devised by DARPA (Defense Advanced Research Projects Agency) to promote research in the area of autonomous vehicles. The challenge consists of building a robot capable of navigating 175 miles through desert terrain in less than 10 hours, with no human intervention.
  • 8.
  • 9.
  • 10.
  • 11. Supervised Learning I Example: Imagine you want to classify versus Data : 100 monkey images and 200 human images with labels what is what. where x represents the greyscale of the image pixels and y=0 means “monkey” while y=1 means “human”. Task : Here is a new image: monkey or human?
  • 12.
  • 13. 1NN Decision Surface decision curve
  • 14.
  • 15.
  • 16.
  • 17.
  • 18. The logit / sigmoid Determines the offset Determines the angle and the steepness.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 28.
  • 29. Generalization Learning is concerned with accurate prediction of future data, not accurate prediction of training data. (The single most important sentence you will see in the course)
  • 30.