C. V. Raman Global University
Assignment
2
Branch
CSE
Programme
B. Tech
Course
Name
Machine Learning
Semester
5
CSE23317
Course Code
Learning
Level (LL)
Academic Year
2022-23
L1: Remembering
L3: Applying
L5: Evaluating
L2: Understanding
L4: Analyzing
L6: Creating
Q’s
Questions
Co
s
LL
CO
2
L1,
L2,
L4, L5
CO
L1,
L2,
L4, L5
Refer to the below Table:
Student
Name
Mathematics
Computer
Science
Result
Aryan
4
3
FAIL
Megha
6
7
PASS
Ravi
7
8
PASS
Rahul
5
5
FAIL
Shreya
8
9
PASS
Nisha
6
8
?
1.
As per the above Table, find whether Nisha has Passed the test or
failed, using KNN (K=3).
2.
ISRO wants to discriminate between Martians (M) and Humans
(H) based on the following features: Green ∈ {N, Y}, Legs ∈ {2,3},
Height ∈ {S, T}, Smelly∈ {N, Y}. The training data is as follows.
Which attribute will be the root of the decision tree? Create the
decision tree and predict the species (class label) for the test
data<Green=Y, Legs=3, Height=T, Smelly=Y>
3.
Write the steps of Perceptron Algorithm. Explain perceptron algorithm
with “OR”. Show 5 updates of perceptron algorithm for the following
data, starting w=(0,0):{(-1,(1,-4)),(+1,(5,-1)),(+1,(3,3)),(+1,(-1,5)),(-1,(5,2)),(-1,(-2,-2))}.
CO
2
L1,
L2,
L4, L5
CO
2
L1,
L2,
L4, L5
CO
2
L1,
L2,
L4, L5
Elaborate Logistic Regression. Given a list of patients with their
respective age ( ) and cholesterol level ( ):
4.
5.
➢ For each patient, calculate the predicted probability that
the patient has the disease.
➢ Classify each patient using a threshold of 0.5 and compare
the predictions with the actual labels.
The sales of a company (in million dollars) for each year are
shown in the table below.
➢
➢
Find the least square regression line y = a x + b.
Use the least squares regression line as a model to
estimate the sales of the company in 2012.
You have a small dataset with 8 data points. You want to perform
4-fold cross-validation using the k-NN algorithm to classify the
data and find average accuracy of the model.
6
CO
2
L2
CO
2
L2,L3
Check the following MLP and dataset where learning rate is 0.5.
Update the weights using backpropagation algorithms.
7
Create a linear Regression model that predict the price of the new
house.{Solve the first updated value of the learning co-efficient.}
Note: Initial Value of β0=0.01 and β1=0.02,α=0.01.
9
House Size(X) Sq. ft
House Price(Y) $
1100
19900
1400
24500
1425
31900
1550
24000
CO
2
L1,L3
Submission Instructions:
1. Submit a write-up document in yellow color assignment note copy with the desired results
(Physical Mode). Submission dead line.
15/09/2024
Note:
Course
Outcomes
CO1
Use mathematical concepts required for machine learning
CO2
Identify and differentiate different types of supervised learning
CO3
Identify and differentiate different types of unsupervised learning
CO4
Apply learning mechanisms like Bayesian Classifier, SVM etc.
CO5
Explore advanced methods of machine learning