Assignment 1:
The Solutions of this questions are given in Document Below:
1 What is Machine Learning? Write five applications it.
2 Explain Types of Learning in details.
3 Explain types of Data in ML.
4 Explain three different part of dataset.
5 Explain following performance measures : Accuracy , FPR , FNR, TPR and TNR.
6 What is Feature Engineering? Explain Feature Subset Selection.
7 Explain PCA with example.
8 What is regression? Explain types of Regression.
9 What is Maximum Likelihood of estimation?
10
Calculate the two regression equations of X on Y and Y on X from the data given below,
taking deviations from a actual means of X and Y.
Estimate the likely demand when the price is Rs.20
11
Obtain regression equation of Y on X and estimate Y when X=55 from the following
12
Find the means of X and Y variables and the coefficient of correlation between them from
the following two regression equations:
2Y–X–50 = 0
3Y–2X–10 = 0
Assignment 2:
The Solutions of this questions are given in Document Below:
1. Suppose that the data mining task is to cluster the following eight points (with (x, y)
representing location) into three clusters: A1(2, 10), A2(2, 5), A3(8, 4), B1(5, 8), B2(7, 5),
B3(6, 4), C1(1, 2), C2(4, 9): The distance function is Euclidean distance. Suppose initially
we assign A1, B1, and C1 as the center of each cluster, respectively. Use the k-means
algorithm to show
1) The three cluster centers after the first-round execution
2) The final three clusters
2. Explain Bayesian Belief Network with example.
3. The decision on whether tennis can be played or not is based on the following features:
Outlook E {Sunny, Overcast, Rain}, Temperature E {Hot, Mild, Cool}, Humidity E
{High, Normal} and Wind E {Weak, Strong}. The training data is given below. Find the
Entropy of entire dataset and Information Gain for all attributes.
4. Explain Back propagation with example.
5. What are Hyperparameters tuning in Deep Learning?
6. Explain Recurrent Neural Networks and its applications.
7. Explain Deep Reinforcement Learning.
8. Explain Adversarial Attacksin deep learning.
9. How can distance be computed for attributes that having missing valves in K-Nearest
Neighbor classifier?
10. Explain the following as attribute selection measure:
(i) Information Gain
(ii) Gain Ratio
11. Explain Prepruning and Postpruning with an example.
12. Calculate 2 clusters using k-means cluster algorithm. For finding the distance use
Euclidian distance.
Assume mean1 as subject1 and mean2 as subject.
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