1. ECE 471/571 - Lecture 19 Review 11/12/15

2. A Roadmap 2 Pattern Classification Statistical ApproachNon-Statistical Approach SupervisedUnsupervised Basic concepts: Distance Agglomerative method Basic concepts: Baysian decision rule (MPP, LR, Discri.) Parameter estimate (ML, BL) Non-Parametric learning (kNN) LDF (Perceptron) k-means Winner-take-all Kohonen maps Dimensionality Reduction FLD, PCA Performance Evaluation ROC curve ( TP, TN, FN, FP ) cross validation Classifier Fusion majority voting NB, BKS Stochastic Methods local opt (GD) global opt (SA, GA) Decision-tree Syntactic approach NN (BP, Hopfield, DL) Support Vector Machine

3. 3 Q&A What is the common feature of statistical pattern classification? What is supervised pattern classification? How is it different from unsupervised classification? When performing classification, what is the general rule that Baysian decision methods follow? (MPP, Likelihood ratio, Discriminant function) What is the difference between parametric classification and non-parametric classification? What is maximum likelihood estimation? Can you verbally describe kNN? And possible improvements? Both FLD and PCA reduce the dimension of a dataset. However, different projection directions are generated from the two approaches. Comment on the fundamental differences. Learn to plot and use and explain an ROC curve Be familiar with the relationship between TP, FP, TN, FN, precision, recall, specificity, sensitivity, and accuracy

4. 4 Q&A - Cont ’ d What ’ s the difference between feed-forward and feed-backward NN? Examples of each? What is LDF? How to extend from 2-class case to multi-class separation? What ’ s the problem with c 2-class separation and c(c-1)/2 2-class separation? Performance wise, what is the difference between perceptron and bp? Pros and cons How do they work? (need to know the detailed procedure) (no proof needed) Know of different ways to improve the performance of bp  E.g. what is sigmoid? Why is it better?  What is optimal learning rate? Why? The biological structure of a neuron

5. Q&A – Cont’d Stochastic method What is gradient descent? Know the detailed algorithm. What ’ s the problem? How do SA and GA achieve global optimum as compared to the principle of GD? GA (natural selection with binary tournament mating, exploration with recombination and mutation) Clustering What is unsupervised learning? What i s the difference between dmin, dmax, and dmean? What is agglomerative clustering? Understand k-means, winner-take-all, and Kohonen maps Performance evaluation approaches What is confusion matrix? Fusion Majority voting Naïve Bayes 5

6. 6 Forming Objective Functions Maximum a-posteriori probability Maximum likelihood estimate Fisher ’ s linear discriminant Principal component analysis kNN Perceptron Back propagation neural network Hopfield network ROC curve Various clustering algorithm NB

7. 7 Distance Metrics Minkowski distance Manhattan distance (city-block distance) Euclidean distance Mahalanobis distance Distance between clusters (dmin, dmax, vs. dmean) Classify the following samples into two clusters using dmin, dmax, and dmean. The samples are (0,1), (0,2), (0,3), (0,4), (0,5), (0,6), (0,7), (0,8)

8. Course evaluation (5 pts) counted in Test 2. 8