BDS602 – AIML Important Questions with answers

BDS602 – Artificial Intelligence and Machine Learning Solved Important Questions with answers

Module – 1

1] What is Artificial Intelligence? Discuss the 4 categories of AI, foundations and history of AI.

2] Identify the Turing test approach to provide a satisfactory operational definition of intelligence.

3] Explain the concept of Intelligent Agents. Discuss agents, environments, rationality, and the structure of agents

4] Simple reflex agents

5] Vacuum Cleaner World

Module – 2

1] Explain the concept of a Problem-Solving Agent. How does it operate?

2] What is uninformed search? Explain breadth-first and depth-first search strategies with their characteristics.

3] What are Heuristic Functions? Discuss admissibility and examples in the 8-puzzle problem.

4] Toy Problem and 8 Queens

5]

Module 3

1] Explain the need for Machine Learning in modern organizations. What factors have contributed to its growing popularity?

2] What is Machine Learning? Explain how it differs from traditional programming. Discuss the process of learning in humans and machines with the help of examples.

3] Explain how Machine Learning is related to other major fields [or] Define Machine Learning. Explain its relationship to other fields with diagram

4] Explain different types of machine learning with a diagram

5] Explain the challenges of Machine learning. [or]Explain the major challenges faced in the implementation of Machine Learning systems.

6] Explain the Machine Learning Process model/ Data Mining Process. (with the help of CRISP-DM model.)

7] List and explain any four real-world applications of Machine Learning.

8] Define data. Explain the 6 V’s of Big Data. / Elements of Big Data

9] What is Big Data Analytics? Explain the types of data analytics and the Big Data Analytics Framework.

10] Explain data preprocessing with an example. / Explain data collection and data preprocessing in big data analytics. Also describe different data cleaning and normalization techniques.

11] What is Descriptive Statistics? Explain different types of data with examples.

Module 4

1] Explain the importance of Statistics and Probability in Machine Learning. Briefly describe different types of probability distributions used. /
[or] Explain continuous and discrete probability distributions

2] Apply and explain Principal Component Analysis algorithm for the given data points and prove that PCA works.

3] Apply Singular Value Decomposition (SVD) to the given matrix and explain the steps involved.

4] Explain the design of a learning system / Design a learning system for chess game

5] Apply the Find-S algorithm to the given training dataset and explain the hypothesis generation process. Mention the limitations of the Find-S Algorithm.

6] Explain Version Spaces and the Candidate Elimination Algorithm.
Apply the algorithm to the given dataset and show how the version space is derived.

7] Explain and apply Candidate Elimination Algorithm for the given dataset.

8] Explain k-Nearest Neighbor (k-NN) algorithm. Using the student dataset given below, classify a new instance (6.1, 40, 5) using k-NN with k = 3. Show all calculations.

9] Define Weighted k-Nearest Neighbor algorithm. Apply weighted KNN algorithm using the given dataset to classify the test set data (7.6, 60,8) where k=3

Module 5

1] Explain the concept of Artificial Neurons. Describe the Simple model of an Artificial Neuron (McCulloch and Pitts Model.)

2] Define Activation Function. Explain different types of activation functions.

3] Explain the Perceptron Model and its Learning Algorithm.

4] Analyze different types of Artificial Neural Networks with a diagram.

5] Explain the Applications, Advantages, Disadvantages, and Challenges of Artificial Neural Networks (ANNs).