Q-1: briefly explain each of the following concepts:
Learning
Supervised learning
Unsupervised learning
Reinforcement learning
Decision trees
Overfitting and underfitting
Entropy
Information gain
Linear regression
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.
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)}.
Find the weights, w0 and w1, for the univariate linear function h(x) = w1*x+w0 that minimizes the squared
loss function.
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.