Artificial Intelligence. Programming Styles

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1 Artificial Intelligence Intro to Machine Learning Programming Styles Standard CS: Explicitly program computer to do something Early AI: Derive a problem description (state) and use general algorithms to solve it Search: set a search state, generate successors Logic: state facts as sentences, use logical inference rules to derive consequences Planning: create initial state, goal state, and action schemas, use forward-chaining and backward-chaining to create plans 1

2 Learning Agent improves its performance by adjusting its own models Goal of discovering relationships (patterns) between input and output What features are best in mapping input to output? Need to recognize what s important and what is not Discover properties of the environment Various Learning Situations Clustering Find useful groupings of data (e.g., height/weight) Classification Identify hand-written digits Filter mail into spam/not-spam Find face in a pic Action Robot balance upright on legs Autopilot fly level Vehicle stay in lane 2

3 What is Clustering? Grouping together items that are similar Similarity determined via given measurement of closeness or proximity Items from same cluster should be more similar to each other than to items from different clusters Selecting appropriate data representation and proximity measurement imperative Referred to as Unsupervised Learning Do not have the known (ground-truth) clusters to evaluate against Cluster/Grouping Learning 3

4 Cluster/Grouping Learning How many groups and who belongs to which group? No Best Clustering 4

5 No Best Clustering No Best Clustering 5

6 No Best Clustering No Best Clustering Need to specify objective function/evaluation criteria to compare clusterings Internal Criteria: quantify quality of clustering based on data itself External Criteria: quantify quality of clustering based on ground-truth labels (but usually do not have these!) 6

7 K-Means Clustering K-Means ( Grouping/Clustering ) One of the simplest clustering algorithms, yet widely employed Given initial set of K centroids/means (generally obtained through initialization with random data points or locations): Assign each point to closest (generally Euclidean) centroid Re-compute centroid/mean locations based on current assignments Repeat until convergence or maximum number of iterations Works well under constrained conditions 7

8 K-Means Appropriate Seeding 8

9 K-Means Appropriate Seeding K-Means Inappropriate Seeding 9

10 K-Means Inappropriate Seeding K-Means Comments Works well when clusters are compact and well-separated Results dependent on initial conditions Often run multiple times and keep clustering minimizing sum of squared distances (points to centroids) Need to know number of clusters K a priori Does not always perform well 10

11 K-Medoids Related to K-Means Instead of using mean to represent a cluster, use a particular example in the cluster Method: Select initial medoids (random examples) Repeat until convergence or maximum number of iterations: In each cluster, make the example that minimizes the sum of distances within the cluster the medoid Reassign each point to the cluster having the closest medoid (from the previous step) More robust to noise and outliers (as compared to K- Means) as it minimizes a sum of pairwise dissimilarities instead of a sum of Euclidean distances Supervised Learning 11

12 Learning Hmmm which objects are boxes? Courtesy NASA/JPL-Caltech Supervised Learning Hmmm which objects are boxes? Courtesy NASA/JPL-Caltech yes no yes no no 12

13 Supervised Learning Given: training data Set of data with corresponding class labels Needs to be representative of entire dataset Objective: build a classifier to predict output labels (classes) of data in unseen test set Need to infer a function that separates the data into desirable classes May need to tune algorithm parameters Feature representation is important Again, no single algorithm works best on all datasets Supervised Learning Process Split data into training and testing sets Determine features to employ Select a classifier Train the classifier using the training set Classify the test set Evaluate the results 13

14 Evaluating Supervised Learning Training data must be selected so as to reflect the global data pool Testing on unseen data is crucial to prevent overfitting to the training data Unintended correlations between input and output e.g., Recall photos with tanks taken on sunny days from AI video 1! Correlations specific to the set of training data e.g., language processing trained on Wall Street Journal or CNN transcripts may not work well for spoken conversation Training Classifiers How to tune algorithm parameters? Use Validation Train classifier on a subset of the training data Examine the classifier on the remaining training data Called the validation set Tune the classifier to minimize the error on the validation set 14

15 Training Classifiers (continued) How to tune? (continued) m-fold Cross-Validation Set classifier options e.g., number of parameters, model form, training time, input features, etc. Estimate generalized classifier performance Randomly divide training set into m disjoint sets of equal size Train using (m-1) subsets and validate on the remaining subset Repeat m times, using different validation set each time Average results Repeat entire process for different classifier options and choose the options which maximize the average results Evaluation Accuracy = Number of correct classifications Number of classifications Consider the 2-class case where we are trying to detect instances of class X within a dataset containing instances from X and Y True Positive (TP) Correctly classifying an instance of X as X False Positive (FP) Incorrectly classifying an instance of Y as X False alarm or Type I error True Negative (TN) Correctly classifying an instance of Y as Y False Negative (FN) Incorrectly classifying an instance of X as Y Misdetection or Type II error 15

16 Evaluation Precision = Number of correctly detected events TP = Number of detected events TP + FP Recall = Number of correctly detected events TP = True number of events TP + FN F β Measure = 2 ( + β ) 1 2 Precision Recall β Precision + Recall Common to use β = 1 Harmonic mean between precision and recall k-nn 16

17 k-nearest Neighbor One of the simplest supervised classification strategies Algorithm: Compute distance from test sample to labeled samples in the training set Assign test sample the label most common across the first k nearest neighbors from the training data k typically small and odd numbered (no ties) rd + + 2nd 5th x 1st 4th o o o o o o o o o o K=1 yields X is class o K=3 yields X is class + K=5 yields X is class o 17

18 Example Training data (two Gaussians): Class 1 has 667 points sampled from Class 2 has 333 points sampled from 0 5 N, N, Priors: P(Class1) = 2/3 P(Class2) = 1/3 Example Testing dataset consists of 2000 points sampled from same distributions with same priors (2/3 vs. 1/3) 18

19 Example k-nn Classify each testing point using nearest neighbors from training data (known classes) k = 1 k = 3 k = 5 Accuracy = Accuracy = Accuracy = Decision Tree 19

20 Decision Trees Input: Features per example Output: Classification label Learns by subdividing the data into clusters with same properties Good at determining which features are good discriminators Decision Tree Classify pattern through sequence of questions Easy to interpret Branches Leaf nodes 20

21 CART Classification and Regression Trees (CART) General framework for creating decision trees How many splits? Every non-binary numeric decision can be represented as combination of binary decisions x x -1 x 1-1 < x < 1 x x -1 x > -1 x x < 1 x 1 21

22 How Determine the Split? Prefer decisions that lead to simplest tree (Occam s Razor) Want property to split data into purest groups possible Use impurity measure Node N Entropy Impurity i Proportion of patterns at node N that belong to class ω j ( N) = P( ω j ) log2 P( ω j ) j P(ω i ) P(ω i ) ω 1 ω 2 ω 3 ω 4 Low Impurity High Impurity Choose decision at node N that decreases impurity the most Maximize Δi( N) = i( N) [ P i( N ) + ( 1 P ) i( N )] L Want << i(n) L L R ω 1 ω 2 ω 3 ω 4 N Proportion of patterns that go out to node N L Impurity of patterns at node N L N L N R When to stop? Methods exist to determine when to stop splitting But may declare a node a leaf (terminal) too early Alternative: grow tree out entirely (each leaf perfectly pure) and then prune Pruning: Work bottom-up Compute the increase in impurity if two child nodes linked to common parent node are eliminated Merge if increase in impurity is negligible 22

23 How to assign categories to leaf nodes? Simplest approach is to take majority vote of class labels at leaf node Ideally there will be one dominant class Potential options when tie occurs: Random assignment Take into account priors Take into account classification risks Cost of misdetections or false alarms of categories Using same training data from before 23

24 Example Decision Tree (Matlab) Mostly Full Tree (nodes must have at least 10 observations to be split) x < or x y < or y Example Decision Tree Slightly Pruned Tree 24

25 Example Decision Tree Moderately Pruned Tree Example Decision Tree Heavily Pruned Tree 25

26 Example Using Test Data Mostly Full Tree Slightly Pruned Tree Accuracy = Accuracy = Moderately Pruned Tree Highly Pruned Tree Accuracy = Accuracy = Neural Networks A Neural Network is another example of a supervised learning classifier More (much more) coming soon! 26

27 Summary Unsupervised learning K-Means, K-Medoids Supervised learning Training and evaluation k-nn Majority vote of nearest neighbors in training data Decision Trees CART 27

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