Introduction to Machine Learning: Assignment 1

1)  Which of the following is a supervised learning problem?

a. Predicting the outcome of a cricket match as win or loss based on historical data. 

b. Recommending a movie to an exisiting user on a website like IMdB based on the search history (including other users)

c. Predicting the gender of a person from his/her image. You are given the data of 1 Million images along the gender

d. Given the class labels of old news articles, predicting the class of a new news article from its content. Class of a news article can be such as sports, politics, technology, etc

Ans: a and c2) Which of the following are classification problems?
a. Predicting the temperature (in Celsius) of a room from other environmental features (such as atmospheric pressure, humidity etc)
b. Predicting if a cricket player is a batsman or bowler given his playing records
c. Finding the shorter route between two existing routes between two points.
d. Predicting if a particular route between two points has traffic jam or not based on the travel time of vehicles
Ans: b and d3) Which of the following is a regression task?
a. Predicting the monthly sales of a cloth store in rupees
b. Predicting if a user would like to listen to a newly released song or not based on historical data
c. Predicting the confirmation probability (in fraction) of your train ticket whose current status is waiting list based on historical data
d. Predicting if a patient has diabetes or not based on historical medical records.
Ans: a and c4) Which of the following is an unsupervised task?
a. Grouping images of footwear and caps separately for a given set of images
b. Learning to play chess
c. Predicting if an edible item is sweet or spicy based on the information of the ingredients and their
quantities.
d. all of the above
Ans: a
5) Which of the following is a categorical feature?
a. Number of legs of an animal
b. Number of hours you study in a day
c. Branch of an engineering student
d. Your weekly expenditure in rupees.
Ans: c
6) Let X and Y be a uniformly distributed random variable over the interval [0,4] and [0,3]
respectively. If X and Y are independent events, then compute the probability, P(max(X,Y)>2)
a. 1/6
b. 5/6
c. 2/3
d. None of the above
Ans: b or c [doubt]
7) Let the trace and determinant of a matrix   [a b; c d] be 4 and 3 respectively. The
eigenvalues of A are
a. [3+sqrt(7i)]/2 , [3+sqrt(7i)]/2, where i = sqrt(-1)
b. 1,3
c. None of the above
d. Cannot be computed as the entries of the matrix A are not given
Ans: b
8) What would be the ideal complexity of the curve which can be used for polynomial curve
fitting for the data shown below. (y-axis denotes the dependent variable)
a) Linear
b) Quadratic
c) Cubic
d) If there are N training samples, fit a (N − 1) order polynomial for achieving minimum training error
Ans: c
9) Which of the following are true about bias and variance of overfitted and underfitted models?
a) Underfitted models have low bias
b) Underfitted models have high bias
c) Overfitted models have low variance
d) Overfitted models have high variance
Ans: b and d10) What happens when your model complexity increases?
a) Model Bias increases
b) Model Bias decreases
c) Variance of the model increases
d) Variance of the model decreases
Ans: b and c