(SEM VII) THEORY EXAMINATION 2020-21
[Time: 3:00 Hours] Total Marks: 70
Note: Attempt all Sections. If require any missing data: then choose suitably.
- Attempt all questions in brief. 2 x 7 = 14
- What is heuristic function?
- Write the difference between supervised and unsupervised learning.
- List down the characteristics of agent.
- List some of the uniform search technique.
- Differentiate between forward and backward chaining.
- What is bay’s rule?
- Define reinforcement learning.
2. Attempt any three questions in brief. 7 x 3 = 21
- Explain DFS algorithm with suitable example.
- Define a well-formed formula(wff) and List some of the rules of inference.
- What are Statistical learning models? Show with suitable example.
- Define PCA. Differentiate between Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
- Explain state space approach for solving any AI problem.
SECTION – C
3. Attempt any one part of the following: 7 x 1 = 7
- Describe the four categories under which AI is classified with examples.
- List various components of natural language understanding process. Describe syntactic analysis and semantic analysis in brief.
4. Attempt any one part of the following: 7 x 1 = 7
- Explain Alpha-Beta pruning? Solve the following question-
2. Discuss Constraint Satisfaction problem with an algorithm for solving a Cryptarithmetic problem.
5. Attempt any one part of the following: 7 x 1 = 7
- Explain resolution in predicate logic with suitable example.
- Trace the operation of the unification algorithm on each of the following pairs of literals:
- f(Marcus) and f(Caesar)
- f(x) and f(g(y))
- f(Marcus, g(x,y)) and f(x,g(Caesar, Marcus)).
6. Attempt any one part of the following: 7 x 1 = 7
- Define decision tree? Explain it’s with suitable example.
- How can use Expectation-Maximization (EM Algorithm) in machine learning? Explain with appropriate example.
7. Attempt any one part of the following: 7 x 1 = 7
- Give the block diagram of pattern recognition system. Explain in brief.
- What do you mean by support vector machine (SVM)? Explain in detail with suitable example.
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