Homework #2
CS 539,
100 points total [6% of your final grade]
Delivery: Submit via Canvas
For this assignment, you will:
(70 pts) Implement linear regression with gradient descent
(30 pts) Make predictions by using your implementation
Part 1: Implement linear regression with gradient descent
In this problem, you will implement the linear regression algorithm in python3. We provide the following files:
a) linear_regression.py - You will implement several functions. As we discussed in class, implement the
functions by using vectorization. You may refer to matrix calculus here:
https://en.wikipedia.org/wiki/Matrix_calculus
Do not change the input and the output of the functions.
b) test.py - This file includes unit tests. Run this file by typing ‘nosetests -v test.py’ in the terminal as you
did in homework 1 in order to check whether all of the functions are properly implemented. No
modification is required.
Part 2: Make predictions by using your implementation
Given training and test sets, you will make predictions of test examples by using your linear regression
implementation (linear_regression.py). We provide the following file:
a) application.py – write your code in this file. Do not change X and y.
Please play with the parameters alpha and number of epochs to make sure your testing loss is smaller than 1e-2
(i.e., 0.01). Report your parameters, training loss and testing loss. In addition, based on your observations,
report a relationshp between alpha and number of epochs. Note that a single epoch means the single time you
see all examples in the training set.
What to turn in:
Submit to Canvas your linear_regression.py, application.py and a pdf document for part 2.
This is an individual assignment, but you may discuss general strategies and approaches with
other members of the class (refer to the syllabus for details of the homework collaboration
policy).
