Description
1 Question 1 – Probability (30 points)
Let X1 and X2 be independent continuous random variables uniformly distributed from 0 to 1. Let X =
max(X1, 2X2). Compute:
1. The expectation E(X)
2. The variance V ar(X)
3. The covariance: Cov(X, X1).
2 Question 2 – Feature examination (25 points)
Analyzing the range and distribution of feature values are important in machine learning. In this question,
you are asked to write a function to approximate the distribution of each individual features by an univariate
Gaussian distribution.
2.1 Question 2.1 (10 points)
Write a Python file hw1.py that contains a function with the following signature:
[mu0, var0, mu1, var1] = get mean and variance(X, y)
where
Inputs:
• X: a two dimensional Numpy array of size n × d, where n is the number of data points, and d the
dimension of the feature vectors.
• y: a Numpy vector of length n. y[i] is a binary label corresponding to the data point X[i, :].
Outputs:
• mu0: a Numpy vector of length d, mu0[j] is the mean of X[i, j] for all i where y[i] = 0. Basically,
mu0[j] is the mean of the j
th feature for all the negative data points.
• var0: a Numpy vector of length d, var0[j] is the variance of X[i, j] for all i where y[i] = 0.
• mu1: a Numpy vector of length d, mu1[j] is the mean of X[i, j] for all i where y[i] = 1.
• var1: a Numpy vector of length d, var1[j] is the variance of X[i, j] for all i where y[i] = 1.
2.2 Question 2.2 (15 points)
For this question, you will use the data provided in the file covid19 metadata.csv. This is a subset of the
COVID-19 image data collection (github.com/ieee8023/covid-chestxray-dataset).
1
Inspect your data carefully: Each row corresponds to a patient, which was suspected positive for COVID-19.
The first column corresponds to their age (continuous value) and the second one to their gender (F for female
or M for male). The last column includes information if they recovered (Y) or not (N).
The first step is to load the data and pre-process them. (Tip: To load the data from the provided csv file,
you can use numpy.genfromtxt or csv.reader). In this case, the feature matrix X should include the first two
columns, namely the age and gender of the patients. You will need to convert their gender to integer values
(e.g. 1 for female and 0 for male). The labels vector y is the last column. You will need to convert it to
integer values, namely 1 for Y (survived) and 0 for N (not survived).
Then, run the function of the Question 2.1 on this data.
(a) (5 points) Report the values of mu0, var0, mu1, var1.
(b) (5 points) For each feature j, plot the Gaussian distribution with mean mu0[j] and variance var0[j] in
black color. On the same graph, plot the Gaussian distribution with mean mu1[j] and variance var1[j]
in blue. You can use Python packages matplotlib and scipy.
(c) (5 points) Is it a good idea to approximate gender by a Gaussian distribution? Why or why not?
3 Question 3 – Linear Regression (45 points)
In this question, you will use Linear Regression to forecast the number of COVID-19 deaths for the next day
based on the numbers of the total cases and deaths in the last seven days. Suppose you have collected the data
for the past n days (x1, y1),(x2, y2), . . . ,(xn, yn), where xi and yi are the number of the aggregated cases
and deaths for day i respectively. You hypothesize that there is a linear relationship between the number of
deaths for day t based on the data from the previous days, and you derive a model that predict the number of
deaths for day t based on:
yˆt =
X
7
i=1
wixt−i +
X
7
i=1
w7+iyt−i + b, for 8 ≤ t ≤ n. (1)
3.1 Question 3.1 (25 points)
Write a Python file hw1.py that contains a function with the following signature:
[w, b] = learn reg params(x, y)
where
Inputs:
• x: a Numpy vector of length n, where n is the number of days
• y: a Numpy vector of length n
Outputs:
• w: a Numpy vector of length 14
• b: a scalar value for the intercept of Linear Regression model.
Tip: you can use sklearn.linear model.LinearRegression from the scikit-learn library.
2
3.2 Question 3.2 (20 points)
The file covid19 time series.csv contains the dataset that you will use. The first row is the csv header. The
second row contains the aggregated counts of the confirmed positive cases in the US. The third row contains
the aggregated counts of deaths in the US. These data are a subset of “JHU CSSE COVID-19 Data” gathered
at Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19). The first step
is to load the data into x and y. Use the function that you wrote in Question 3.1 to calculate the parameters
w and b of Linear Regression model.
(a) (5 points) Report the learned parameters: the weights w and the intercept term b.
(b) (5 points) Visualize the actual and predicted death values yt and yˆt (for 8 ≤ t ≤ n). Display yt as a
function of t and yˆt as a function of t on the same graph. You can use the library matplotlib.pyplot to
plot.
(c) (5 points) Use a Gaussian to approximate the distribution of the errors yt − yˆt (for 8 ≤ t ≤ n). Report
the mean and variance of this Gaussian.
(d) (5 points) Use matplotlib.pyplot.hist to plot the distribution of yt − yˆt (for 8 ≤ t ≤ n). On the same
plot, plot the Gaussian function that approximates this distribution. Is Gaussian a good approximation
for the distribution of the errors?
4 What to submit
You will need to submit both your code and your answers to questions on Blackboard. Put the answer file
and your python code in a folder named: SUBID FirstName LastName (e.g., 10947XXXX Barack Obama).
Zip this folder and submit the zip file on Blackboard. Your submission must be a zip file,
i.e, SUBID FirstName LastName.zip.
The answer file should be named: hw1-answers.pdf. You can use Latex if you wish, but it is not compulsory.
The first page of the hw1-answers.pdf should be the filled cover page at the end of this homework. The
remaining of the answer file should contain:
1. Answers to Questions 1
2. Answers to Questions 2.2
3. Answers to Questions 3.2
Your Python code must be named hw1.py. It should contains two functions in Question 2.1 and Question
3.1. For automated testing, it should be possible for us to import your functions using ‘from hw1 import
learn reg params, get mean and variance’. You can submit other python/data files if necessary. Your code
hw1.py can also include other functions if necessary.
Make sure you follow the instructions carefully. Your will lose points if:
1. You do not attach the cover page. Your answer file is not named hw1-answers.pdf
2. Your functions do not have the exact signatures as instructed
3. Your functions cannot be imported for automatic testing
4. Your functions crash or fail to run
5 Cheating warnings
Don’t cheat. You must do the homework yourself, otherwise you won’t learn. You cannot ask and discuss
with students from previous years. You cannot look up the solution online.
3
Cover page for answers.pdf
CSE512 Spring 2021 – Machine Learning – Homework 1
Your Name:
Solar ID:
NetID email address:
Names of people whom you discussed the homework with:
