Epilogue: what have you learned this semester?

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1 Epilogue: what have you learned this semester? ʻViagraʼ =0 =1 ʻlotteryʼ ĉ(x) = spam =0 =1 ĉ(x) = ham ĉ(x) = spam

2 What did you get out of this course? What skills have you learned in this course that you feel would be useful? What are the most important insights you gained this semester? What advice would you give future students? What was your biggest challenge in this course? What would you like me to do differently? 2

3 What I hope you got out of this course The machine learning toolbox Formulating a problem as an ML problem Understanding a variety of ML algorithms Running and interpreting ML experiments Understanding what makes ML work theory and practice

4 Learning scenarios we covered Classification: discrete/categorical labels Regression: continuous labels Clustering: no labels

5 Supervised learning A variety of learning tasks Unsupervised learning Semi-supervised learning Access to a lot of unlabeled data Multi-label classification Each example can belong to multiple classes Multi-task classification Solving multiple related tasks

6 A variety of learning tasks Outlier/novelty detection Novelty: anything that is not part of the normal behavior of a system. Reinforcement learning Learn action to maximize payoff Structured output learning 2. Hints (invariances an rotational invariance, m 3. Removing noisy data symmetry intensity 4. Advanced validation More efficient than CV, brace c AM L Creator: Malik Magdon-Ismail

7 Learning in structured output spaces Handle prediction problems with complex output spaces Structured outputs: multivariate, correlated, constrained General way to solve many learning problems Examples taken from Ben Taskar s 07 NIPS tutorial

8 Local vs. Global Global classification takes advantage of correlations and satisfies the constraints in the problem brace

9 Other techniques ² Graphical models (conditional random fields, Bayesian networks) ² Bayesian model averaging 9

10 The importance of features and their representation Choosing the right features is one of the most important aspects of applying ML. What you can do with features: ² Normalization ² Selection ² Construction ² Fill in missing values 10

11 Types of models Geometric q q Ridge-regression, SVM, perceptron Neural networks w w T x + b > 0 Distance-based q K-nearest-neighbors w T x + b < 0 Probabilistic q Naïve-bayes P (Y = spam Viagara, lottery) ʻViagraʼ Logical models: Tree/Rule based q Decision trees ʻlotteryʼ =0 =0 =1 =1 ĉ(x) = spam Ensembles 11 ĉ(x) = ham ĉ(x) = spam

12 Loss + regularization Many of the models we studied are based on a cost function of the form: loss + regularization Example: Ridge regression 1 N NX (y i w x i ) 2 + w w i=1 12

13 Loss + regularization for classification SVM C N NX i=1 max [1 y i h w (x i ), 0] w w Hinge loss L 2 regularizer The hinge loss is a margin maximizing loss function Can use other regularizers: w 1 (L 1 norm) Leads to very sparse solutions and is non-differentiable. Elastic Net regularizer: w 1 +(1 ) w

14 Loss + regularization for classification SVM Logistic regression 1 N NX i=1 C N NX i=1 max [1 Hinge loss Log loss y i h w (x i ), 0] w w L 2 regularizer log(1 + exp(y i h w (x i )) + 2 w w L 2 regularizer AdaBoost can be shown to optimize the exponential loss 1 N NX exp( y i h w (x i )) i=1 14

15 Loss + regularization for regression Ridge regression i=1 Closed form solution; sensitivity to outliers Lasso 1 N 1 N Sparse solutions; non-differentiable NX (y i w x) 2 + w w NX (y i w x) 2 + w 1 i=1 Can use alternative loss functions 15

16 Comparison of learning methods TABLE Some characteristics of different learning methods. Key: = good, =fair, and =poor. Characteristic Neural SVM Trees MARS k-nn, Natural handling of data of mixed type Nets random forests Kernels Handling of missing values Robustness to outliers in input space Insensitive to monotone transformations of inputs Computational scalability (large N) Ability to deal with irrelevant inputs Ability to extract linear combinations of features Interpretability Predictive power Table 10.1 from Elements of statistical learning 16

17 Comparison of learning methods TABLE Some characteristics of different learning methods. Key: = good, =fair, and =poor. Characteristic Neural SVM Trees MARS k-nn, Natural handling of data of mixed type Nets Kernels Handling of missing values Robustness to outliers in input space Insensitive to monotone transformations of inputs Computational scalability (large N) Ability to deal with irrelevant inputs Ability to extract linear combinations of features Interpretability Predictive power Table 10.1 from Elements of statistical learning 17

18 The scikit-learn algorithm cheat sheet 18

19 19

20 Applying machine learning Always try multiple models What would you start with? If accuracy is not high enough Design new features Collect more data 20

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