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Stanford Machine Learning Course
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Stanford Machine Learning Course
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25 Questions

1. multiclass classification problem

2. high bias

3. regularization parameter

4. Works well when n is large Slow if n is very large

5. Make a plot with number of iterations on the x-axis. Now plot the cost function - J(θ) over the number of iterations of gradient descent. If J(θ) ever increases - then you probably need to decrease α.

6. We are trying to predict results within a continuous output - meaning that we are trying to map input variables to some continuous function.

7. Mean normalization of data to speed up gradient descend

8. positive class

9. True positive

10. overfitting

11. Need to choose alpha No need to choose alpha

12. Machine Learning

13. Version of finding the optimum without iteration. No feature scaling!

14. Categorized into "regression" and "classification" problems.

15. Allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.

16. Trying to fit a linear continuous function to the data. Univariate or Multivariate.

17. One-vs-all classification

18. We are trying to predict results in a discrete output. In other words - we are trying to map input variables into discrete categories.

19. True negative

20. Recall

21. gaussian kernel

22. hidden layer

23. Derive a structure by grouping the data based on relatiship among the variables

24. Declare convergence if J(θ) decreases by less than E in one iteration - where E is some small value such as 10^-3

25. Large data rationale

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