## Description

## Q1 (Linear Regression):

Use the python library (sklearn.linear model) to train a linear regression model for the

Boston housing dataset:

https://towardsdatascience.com/linear-regression-on-boston-housing-dataset-f409b7e4a155.

Split the dataset to a training set (70% samples) and a testing set (30% samples). Report the root mean

squared errors (RMSE) on the training and testing sets.

Q2 Implement the following five algorithms to train a linear regression model for the Boston housing data set

https://towardsdatascience.com/linear-regression-on-boston-housing-dataset-f409b7e4a155

Split the dataset to a training set (70% samples) and a testing set (30% samples). Report the root mean

squared errors (RMSE) on the training and testing sets.

1. The gradient descent algorithm

2. The stochastic gradient descent (SGD) algorithm

3. The SGD algorithm with momentum

4. The SGD algorithm with Nesterov momentum

5. The AdaGrad algorithm

## Q3 (Logistic Regression):

Use the python library (sklearn.linear model) to train a logistic regression model for

the Titanic dataset:

https://blog.goodaudience.com/machine-learning-using-logistic-regression-in-python-with-code-ab3c7f5f3bed.

Split the dataset to a training set (80% samples) and a testing set (20% samples). Report the overall

classification accuracies on the training and testing sets and report the precision, recall, and F-measure scores

for each of the two classes on the training and testing sets.

Q4 (Logistic Regression): Implement the following five algorithms to train a logistic regression model for the

Titantic dataset:

https://blog.goodaudience.com/machine-learning-using-logistic-regression-in-python-with-code-ab3c7f5f3bed.

Split the dataset to a training set (80% samples) and a testing set (20% samples). Report the overall

classification accuracies on the training and testing sets and report the precision, recall, and F-measure scores

for each of the two classes on the training and testing sets.

1. The gradient descent algorithm

2. The stochastic gradient descent (SGD) algorithm

3. The SGD algorithm with momentum

4. The SGD algorithm with Nesterov momentum

1

5. The AdaGrad algorithm

## Q4 (Bonus Question):

You will get an additional full point (1.0) if you can answer this bonus question correctly.

That means, if you answer Q1-Q4 correctly, you get a full point (1.0) for this HW assignment. If you can

answer Q1-Q5 correctly, you will get 2.0 points.

1. Implement the Adam algorithm to train a linear regression model for the Boston housing data set

https://towardsdatascience.com/linear-regression-on-boston-housing-dataset-f409b7e4a155

Split the dataset to a training set (70% samples) and a testing set (30% samples). Report the root mean

squared errors (RMSE) on the training and testing sets.

2. Implement the Adam algorithm to train a logistic regression model for the Titantic dataset:

https://blog.goodaudience.com/machine-learning-using-logistic-regression-in-python-with-code-ab3c7f5f3bed.

Split the dataset to a training set (80% samples) and a testing set (20% samples). Report the overall

classification accuracies on the training and testing sets and report the precision, recall, and F-measure

scores for each of the two classes on the training and testing sets.

2