Machine Learning PYQs

Course Title: Machine Learning
Course Code: PCC-CSE-402-G
Semester: B.Tech. 8th Semester (CSE)

May — 2024 Examination

Short Answer Questions [6 × 2.5 = 15 marks]

1. Answer the following: (a) What are the three main types of Machine Learning classification?
   (b) Define Dimensionality Reduction and why it is important in Machine Learning?
   (c) Explain column vector.
   (d) How do you represent a dataset as a Matrix?
   (e) Discuss two techniques used in Data preprocessing Machine Learning.
   (f) What is PCA, and how does it help with dimensionality reduction?

Unit-I [15 marks]

2. What is machine learning? Describe the features of Machine Learning algorithms and their importance in building predictive models. [15]
3. Create a detailed block diagrammatic representation of learning machines, highlighting the key components and their roles in the Machine Learning process. [15]

Unit-II [15 marks]

4. What is a dataset? Explain with a suitable example. Demonstrate how to represent a dataset as a Matrix. Discuss the advantages of matrix representation in Machine Learning. [15]
5. Describe the process of Data preprocessing in Machine Learning, focusing on Feature Normalization, Mean calculation, column standardization, and Covariance estimation. [15]

Unit-III [15 marks]

6. Define Supervised Learning and explain its working principle. Provide a step-by-step example of how a supervised learning algorithm processes training data and makes predictions. [15]
7. Discuss the importance of labelled data in Supervised Learning and its role in training predictive models. Also, explain Decision Trees by taking a suitable example. [15]

Unit-IV [15 marks]

8. Compare and contrast Boosting, Bagging, and Random Forest as Ensemble Methods in Unsupervised Learning. Discuss how these methods combine multiple models and improve the overall performance of unsupervised learning tasks. Provide insights into scenarios where each ensemble method is most effective. [15]
9. Describe the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) as evaluation metrics for binary classification models. How does the ROC curve visualize the trade-off between a true positive rate and a false positive rate? What insights can be derived from the AUC value? [15]

May — 2023 Examination

Short Answer Questions [6 × 2.5 = 15 marks]

1. Write short notes on: (a) Support Vectors
   (b) Median Absolute Deviation
   (c) F1-score
   (d) K-NN is called Lazy Learner. Explain it.
   (e) Underfitting
   (f) Logistic Regression

Unit-I [15 marks]

2. What is Machine Learning? Explain its importance and applications in the modern world. [15]
3. Discuss different types of Machine Learning techniques with suitable example. [15]

Unit-II [15 marks]

4. (a) What is Dimensionality Reduction?
   (b) Write advantages and disadvantages of Dimensionality Reduction. [15]
5. What is PCA? Write down steps of a PCA algorithm with example. [15]

Unit-III [15 marks]

6. Explain Decision Trees algorithm in ML and discuss its types. [15]
7. What are the basic assumptions used in Naive Bayes classifier? Why is it called Naive Bayes? Also discuss the working of Naive Bayes algorithm. [15]

Unit-IV [15 marks]

8. Differentiate between Bagging and Boosting techniques. [15]
9. Write short notes on: (a) Confusion Matrix
   (b) ROC and AUC [15]

July — 2022 Examination

Short Answer Questions [5 × 3 = 15 marks]

1. Explain the following: (a) Learning
   (b) Reinforcement Learning
   (c) Linear Regression
   (d) Boosting
   (e) Median Absolute Deviation (MAD)

Section-A [15 marks]

2. Define Machine Learning. Explain with examples why machine learning is important. [15]
3. (i) Differentiate between Supervised, Unsupervised and Reinforcement learning. Explain with suitable examples.
   (ii) Discuss any five examples of machine learning applications. [15]

Section-B [15 marks]

4. Compare Feature Extraction and Feature Selection techniques. Explain how dimensionality can be reduced using subset selection procedure. [15]
5. Explain PCA with its advantages and disadvantages. [15]

Section-C [15 marks]

6. What is the goal of the Support Vector Machine (SVM)? Also discuss the margin. [15]
7. (a) Explain Naive Bayes Classifier.
   (b) Explain regression with an example. [15]

Section-D [15 marks]

8. (a) Explain the k-Means Algorithm with an example.
   (b) Describe the Random Forest algorithm to improve classifier accuracy. [15]
9. Explain the following: (i) ROC and AUC
   (ii) Confusion Matrix [15]