SRINIVAS UNIVERSITY
INSTITUTE OF ENGINEERING AND
TECHNOLOGY
MUKKA, MANGALURU
DEPARTMENT OF ARTIFICIAL INTELLIGENCE &MACHINE
LEARNING
QUESTION BANK
FUNDAMENTALS OF MACHINE LEARNING
SUBJECT CODE: 21SAL45
COMPILED BY:
Mss. ANUPAMA, Assistant professor
Machine learning
MODULE 1
1. What is the history of Artificial Intelligence and Machine learning?
2. What machine learning can do for AI?
3. What are the goals of machine learning
4. What are the types of machine learning? Explain
5. Define machine learning limits based on hardware
6. What is the relationship between AI and machine learning?
7. What are the specifications of AI and machine learning
8. Why we need machine learning and how it is different from traditional programming
approaches.
9. Define Machine learning .Discuss with examples some useful applications of machine
learning
10. What are supervised ,semi-supervised and unsupervised learning
11. Describe in detail all the steps involved in designing learning system .Discuss the perspective
the perspective and issues in machine learning
12. Distinguish between Supervised learning and unsupervised learning
MODULE 2
1.Define Big data? What are the sources of big data?
2. Explain data source ?How to build a new data source?
3.What is the role of statistics in machine learning
4.Explain Linear regression algorithm? What are the advantages and disadvantages?
5.Explain Logistic Regression algorithm?What are its assumptions
Anupama,Dept of CSE,SUIET MUKKA Page 1
Machine learning
6.Explain Decision tree algorithm Briefly?
7.Explain Random Forest algorithm? What are the advantages and disadvantages?
8.Describe K-nearest neighbour (KNN) algorithm?
9.Explain k-means algorithm
Anupama,Dept of CSE,SUIET MUKKA Page 2
Machine learning
MODULE 3
1. Explain overview on probabilities and operating on probabilities?
2. Explain conditioning chance by bayes’ theorem?
3. What are the different kinds og learning
4. Explain learning process and optimizing with big data
5. How to demystifying the math behind machine learning
6. Explain math behind machine learning and working with data
7. What is Supervised learning? What are the advantages and disadvatages ?
8. What is UnSupervised learning? What are the advantages and disadvatages ?
9. What is Reinforcement learning? What are the advantages and disadvatages ?
MODULE 4
1.How to train ,validate and test a machine
2.How to avoid sample and leakage traps
3.Explain optimizing by cross-validation
4.How to check out-sample errors
5.Explain validating machine learning by use of example data
6.What are advantages and disadvantages of Training ,validating and Testing
7.Differences between Training ,validating and Testing
MODULE 5
1.What is data and preprocessing data and what are the advantages and disadvantages?
2.How to gather and cleaning a data? Describe it
3.How to repair a missing data and Explain
4.How to measure similarity between vectors and utilizing distances to locate clusters
5. Differences between Data and Preprocessing data?
6. Differences between gathering ,cleaning and repairing missing data
7.How to create own features in data? Explain
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