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Course Content
MODULE 1
0/16
INTRODUCTION
03:56
1. What is Machine learning? Why is Machine learning need for business organization.
06:23
2. Difference between Supervised and Unsupervised Learning
06:17
3. Difference between Labelled and Unlabeled data
06:12
4. Explain any 6 applications of machine learning?
06:11
5. What are the challenges of machine learning? Explain it.
07:06
6. List out different types of Machine learning? Explain Supervised learning with an example.
23:07
8. Difference between Classification and Regression
02:18
9. List out and briefly explain unsupervised algorithm.
05:09
10. What are the key steps involved in the CRISP-DM machine learning process, and how do they contribute to building effective machine learning models?
11. What are the four types of data analytics, and how do they differ in their approach and application?”
04:41
12. What are the four layers of the Big Data Analysis Framework / 4 layer architecture, and what is the role of each layer?
13:24
13. For a given univariate dataset S = {5, 10, 15, 20, 25, 30} of marks, find:Mean,Median,Mode Standard deviation,Variance.
10:03
14. What is Big Data? Explain the types of Big Data.
02:35
15. What is Structured Data? Explain types of it.
04:26
16. Explain Semi-structured and Unstructured data in detail.
06:50
MODULE 2
0/12
1.Explain the process of obtaining principle components and its relevance in feature reduction..
11:32
2. Explain the procedure for pair-t tests and Chi-Square goodness fit test.
04:59
3.Gaussian elimination method
13:42
4.Find LU decomposition of the given matrix
08:48
5. Explain the procedure for hypothesis testing?
08:29
6.Consider the following table
08:35
7.Apply PCA
27:45
8.Explain the key steps involved in the design of a learning system. Why is each step important in building an effective learning algorithm?
04:30
9. Explain hypothesis space search by Find –S Algorithm and Limitations.
07:48
10. Consider the training dataset of 4 instances shown in Table 3.2. It contains the details of the performance of students and their likelihood of getting a job offer or not in their final semester. Apply the Find-S algorithm.
14:19
11. Difference between Bias and Variance
05:54
12. What are the three types of prediction errors in machine learning, and how do they impact model performance?.
05:45
MODULE 3
0/7
1.Define Decision Tree Learning. List and Explain Appropriate Problems for Decision Tree Learning.
06:11
2.Explain the types of Regression methods with diagram.
06:17
3.Explain the advantages and disadvantages of decision tree and its issues.
12:18
4.Explain K-NN algorithm and distance weighted nearest neighbor algorithm.
19:43
5. Let us consider an example where the five weeks’ sales data (in Thousands) is given as shown below in Table 5.1. Apply linear regression technique to predict the 7th and 9th month sales.
13:47
6.Consider the student performance training data the below table.
07:37
7.Apply multiple regression for the values given in Table 5.7 where weekly sales along with sales for products 21 and 22 are provided. Use matrix approach for finding multiple regression.
05:15
MODULE 4
0/9
1.Explain Naïve Bayes Classifier and Bayes Optimal Classifier . Write an algorithm for navie bayes classifier.
10:30
2.Explain Bayes theorem, Maximum A Posteriori (MAP) Hypothesis (MAP) and Maximum Likelihood (ML) Hypothesis (hML).
07:05
3.Given the hypothesis space with 4 hypothesis h₁, h and h₁. Determine if the patient is diagnosed as COVID positive or COVID negative using Bayes Optimal classifier.
28:31
4.Assess a student’s performance using Naïve Bayes algorithm for the continuous attribute.
14:01
5. Illustrate the various types of artificial neural networks.
08:40
6.Explain ANN application, Advantages and disadvantages, limitations & its Challenges.
08:15
7.Explain the different activation functions used in ANN.
14:31
8.Consider a medical diagnosis problem in which there are two alternative hypothesis:
10:14
9.Explain Perceptron model and its algorithm.
16:28
MODULE 5
0/9
1. Difference between Clustering and Classification. Illustrate the applications, challenges of Clustering algorithms.
12:31
2. Explain Q learning and write down the Q learning algorithm
09:26
3. Discuss the learning task and Q-learning in the context of reinforcement learning.
12:18
4. Explain the characteristics, Applications and Challenges of reinforcement learning.
03:39
5. Comparison between reinforcement learning, supervised learning and Unsupervised learning.
15:56
6. Explain SARSA learning and its algorithm.
09:23
7. Explain the basic components of reinforcement learning and reinforcement agent types.
08:02
8. Explain Markov Decision Process with an example.
23:19
9. Suppose that a data mining task is to cluster into three cluster.
08:54
MACHINE LEARNING|| CSE stream || Very Important Questions Explained with Concepts || All 5 modules || Last moment exam (BCS602) preparation
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