Machine Learning Week 3: Linear Regression with one variable

Machine Learning Week 3

Linear Regression with one variable

Poll

Attendance

• Present

• Absent

Multiple Choice

You’re running a company, and you want to develop learning algorithms to address each of two problems.

Problem 1:You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months.

Problem 2: You’d like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised.

Should you treat these as classification or as regression problems?

• Treat both as classification problems.

• Treat problem 1 as a classification problem, problem 2 as a regression problem.

• Treat problem 1 as a regression problem, problem 2 as a classification problem.

• Treat both as regression problems.

Multiple Select

Of the following examples, which would you address using an unsupervised learning algorithm? (Check all that apply.)

• Given email labeled as spam/not spam, learn a spam filter.

• Given a set of news articles found on the web, group them into sets of articles about the same stories.

• Given a database of customer data, automatically discover market segments and group customers into different market segments.

• Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.

Model and Cost Function

Multiple Choice

Consider the training set shown in the image.

 $\left(x^{\left(i\right)},\ y^{\left(i\right)}\right)$   is the  $i^{th}$  training example. What is  $y^{\left(3\right)}$  ?

• 1416

• 1534

• 315

• 0

Multiple Choice

Consider the plot (given in the image) of $h_{\theta}\left(x\right)\ =\ \theta_{0\ }+\ \theta_1x$ 
What are  $\theta_{0\ }$  and  $\theta_1$  ?

•  $\theta_0\ =\ 0,\ \ \theta_1=1$  

•  $\theta_0\ =\ 0.5,\ \ \theta_1=1$  

•  $\theta_0\ =\ 1,\ \ \theta_1=0.5$  

•  $\theta_0\ =\ 1,\ \ \theta_1=1$  

Multiple Choice

Please find the question in the image

• 0

• 1/6

• 1

• 14/6