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:

dt

  1. 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.
  2. The Wiki page lists several implementations of decision-tree learning algorithms. Try one of the implementations on the dataset, and report on your experience.


Q-3: Consider the following set of training examples: {(-2,-1), (1,1), (3,2)}.
  1. Find the weights, w0 and w1, for the univariate linear function h(x) = w1*x+w0 that minimizes the squared loss function.
  2. The blog "Linear Regression with PyTorch" describes how to implement a linear-regression model using Pytorch (source code). Write a program using Pytorch to find a linear model for the above dataset.