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26 Error analysis on the training set

27 Techniques for reducing variance

28 Diagnosing bias and variance: Learning curves

29 Plotting training error

30 Interpreting learning curves: High bias

31 Interpreting learning curves: Other cases

32 Plotting learning curves

33 Why we compare to human-level performance

34 How to define human-level performance

35 Surpassing human-level performance

36 When you should train and test on different distributions

37 How to decide whether to use all your data

38 How to decide whether to include inconsistent data

39 Weighting data

40 Generalizing from the training set to the dev set

41 Addressing Bias and Variance

42 Addressing data mismatch

43 Artificial data synthesis

44 The Optimization Verification test

45 General form of Optimization Verification test

46 Reinforcement learning example

47 The rise of end-to-end learning

48 More end-to-end learning examples

49 Pros and cons of end-to-end learning

50 Learned sub-components

51 Directly learning rich outputs

Page 4 Machine Learning Yearning-Draft Andrew Ng