Some sample midterm questions: 1. Briefly define the following terms: Concept Learning Continuous-Valued Attribute Discrete-Valued Attribute Inductive Learning The Inductive Learning Hypothesis Version Space Inductive Bias Noise N-Fold Cross Validation Training, Testing, Validation (or Tuning) Set Confusion Matrix Confidence Interval ROC Curve Precision Recall Decision Tree Entropy Information Gain Gain Ratio (in decision trees) Overfitting Gradient Descent Artificial Neural Network Linear Threshold Unit Sigmoid Unit Perceptron Multi-Layer Perceptron Batch mode Gradient Descent Incremental or Stochastic Gradient Descent Input Unit Hidden Unit Output Unit 2. Outline the four key questions that must be answered when designing a machine learning algorithm. Give an example of an answer for each question. 3. Define the following algorithms: (a real question would just ask for one of these) Find-S List-Then-Eliminate (Version Space) Candidate Elimination (Version Space) ID3 Perceptron Training Algorithm (assuming linear artificial neurons) Backpropagation (assuming sigmoidal artificial neurons) 4. For each of the algorithms above, show how it works on a specific problem (examples of these may be found in the book or in the notes). 5. Why is inductive bias important for a machine learning algorithm? Give some examples of ML algorithms and their corresponding inductive biases. 6. How would you represent the following concepts in a decision tree: A OR B A AND NOT B (A AND B) OR (C OR NOT D) 7. What problem does reduced-error pruning address? How do we decide when to prunce a decision tree? 8. How do you translate a decision tree into a corresponding set of rules? 9. What mechanism was suggested in class for dealing with continuous-valued attributes in a decision tree? 10. What mechanism was suggested in class for dealing with missing attribute values in a decision tree? 11. What types of concepts can be learned with a perceptron using linear units? Give an example of a concept that could not be learned by this type of artificial neural network. 12. A multi-layer perceptron with sigmoid units can learn (using an algorithm like backpropagation) concepts that cannot be learned by artificial neural networks that lack hidden units or sigmoid activation functions. Give an example of a concept that could be learned by such a network and what the weights of a learned representation of this concept might be. 13. An artificial neural network uses gradient descent to search for a local minimum in weight space. How is a local minimum different from the global minimum? Why doesn't gradient descent find the global minimum? 14. A concept is represented in C4.5 format with the following files. The .names file is: Class1,Class2,Class3. | Classes FeatureA: continuous FeatureB: BValue1, BValue2, BValue3, BValue4 FeatureC: continuous FeatureD: Yes, No The data file is as follows: 2.5,BValue2,100.0,No,Class2 1.1,BValue4,300.0,Yes,Class1 2.3,BValue3,150.0,No,Class3 1.4,BValue1,350.0,No,Class2 What input and output representation would you use to learn this problem using an artificial neural network? Give the input and output vectors for each of the data points shown above. What are the advantages and disadvantages of your representation?