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Regularization
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Classroom Contents
Introduction to Optimization
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- 1 Intro
- 2 What you will learn
- 3 Before we start
- 4 What is the likelihood?
- 5 Example: Balls in urns
- 6 Maximum likelihood estimator
- 7 Example: Coin flips
- 8 Likelihood - Cost
- 9 Back to the urn problem...
- 10 Grid search (brute force)
- 11 Local vs. global minima
- 12 Convex vs. non-convex functions
- 13 Implementation
- 14 Lecture attendance problem
- 15 Multi-dimensional gradients
- 16 Multi-dimensional gradient descent
- 17 Differentiable functions
- 18 Optimization for machine learning
- 19 Stochastic gradient descent
- 20 Regularization
- 21 Sparse coding
