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Machine Learning (As Taught By Andrew Ng)
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Machine Learning (As Taught By Andrew Ng)
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25 Questions

1. A cost function

2. Synonym for Input variable?

3. SSE formula?

4. What is the downside of using an alpha (learning rate) that is too big?

5. Synonym for output variable?

6. Multivariate linear regression

7. What does theta typically represent in stat/ML?

8. Why do we square instead of using the absolute value when calculating variance and standard deviation?

9. Cocktail party effect/problem

10. Why use features that are on a similar scale?

11. Gradient descent

12. Cost function vs Gradient Descent?

13. Regression vs Classification?

14. Some feature scaling methods are

15. 3D Surface Plot - how can it be used to plot the cost function?

16. In stratified k-fold cross-validation

17. How to avoid overfitting?

18. Why is it unnecessary to change alpha over time to ensure that the gradient descent converges to a local minimum?

19. How to make sure gradient descent is working properly?

20. What happens if you initialize a parameter at a local minimum and attempt to use gradient descent on it?

21. Learning algorithm?

22. Classifier?

23. What is feature scaling?

24. Definition of stochastic?

25. Classification

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