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