Introduction
1. Give a definition of ¡°Pattern Recognition¡±.
2. Which of the following problems is a suitable application for pattern recognition?
a. Classifying numbers into primes and non-primes.
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b. Detecting potential fraud in credit card charges.
c. Determining where a cannon ball is likely to land given information about elevation and direction of the barrel, wind direction, etc.
d. Identifying the species a newly discovered ¡°bug¡± belongs to.
3. Give brief definitions of the following terms:
a. Exemplar
b. Dataset
c. Generalization d. Overfitting
e. Decision Theory
f. Feature Space
g. Linearly Separable h. Dichotomizer
i. Hyper-Parameter
j. Grid search k. Training data
l. Test data
4. What types of learning best describe the following three scenarios, in which a coin classification system is being created for a vending machine.
a. Measurements of a large number of coins are taken. The algorithm finds that these measurements fall in several ¡°bins¡±. It finds decision boundaries that separate these bins and uses these to classify new coins.
b. The measurements of each coin is presented to the classifier which makes a decision. The classifier changes its decision boundaries based on whether this decision was correct of incorrect.
c. Measurements of a large number of coins are taken. The algorithm uses this data, together with known class labels, to infer decision boundaries which it then uses to classify new coins.
5. Briefly explain the following types of learning method:
a. Classification
b. Regression
c. Semi-supervised
d. Transfer
Class Features 1 5
6. 2 2 1 4 1 7 2 1
Class Features 1 5.5
Class Features 1 (5,4)
Class Features 1 (5.3,4)
2 (2.3,9.1) 1 (4,3)
2 (1.8,5.5)
Identify which of the above datasets are:
a. univariate-continuous b. multivariate-discrete
c. multivariate-continuous d. univariate-discrete
7. A classifier is designed to determine if a feature vector is or is not in a certain class. The output produced by the classifier is 1 if the sample is predicted to be in the class, and 0 otherwise. The following table shows the feature vectors for the samples in the test set, along with the class labels predicted by the classifier and the true class labels of each sample.
Features Predicted Class (5.3,4) 1
(2.3,9.1) 0
(7,4.1) 1 (1.8,5.5) 0 (6,3.1) 1 (4.5,3.5) 0
True Class 1
Draw a confusion matrix for this data.
8. For the results given in the previous question calculate the following performance metrics:
a. the error-rate b. the accuracy c. the recall
d. the precision e. the f1-score
9. The following table shows exemplars from two classes. Sketch the feature space, and suggest a suitable location for a decision boundary that will minimise the number of mis-classifications. If the cost of erroneously choosing class 1 is higher than the cost of erroneously choosing class two, how will this effect the location of the decision boundary?
Class Features 1 (3,4)
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