HOMEWORK  (Machine Learning)

Q-1: briefly explain each of the following concepts:

1. Learning
2. Supervised learning
3. Unsupervised learning
4. Reinforcement learning
5. Decision trees
6. Overfitting and underfitting
   
7. Entropy
8. Information gain
9. Linear regression
10. Neural networks

Q-2: Consider the following data set comprised of three binary input attributes (Al, A2, and A3) and one binary output:



Use the decision-tree learning algorithm to learn a decision tree for these data. Show the computations made to determine the attribute to split at each node.


Q-3: Given the following set of training examples: {(-2,-1), (1,1), (3,2)}, find the weights, w0 and w1, for the univariate linear function h(x) = w1*x+w0 that minimizes the squared loss function.