Artifical intelligence and machine learning

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ArulmiguMeenakshi Amman College of Engineering
Vadamavandal-604410.
Important questions and answers
OCS351- ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
FUNDAMENTALS
Prepared by
V.Umapathy,
Prof. /Mech.
S.No Question Answer
Syllabus
S.No. Description
1 UNIT I
INTELLIGENT AGENT AND UNINFORMED SEARCH 6
Introduction - Foundations of AI - History of AI - The state of the art - Risks and
Benefits of AI - Intelligent Agents - Nature of Environment - Structure of Agent -
Problem Solving Agents - Formulating Problems - Uninformed Search - Breadth First
Search - Dijkstra's algorithm or uniformcost search - Depth First Search - Depth Limited
Search.
2 UNIT II
PROBLEM SOLVING WITH SEARCH TECHNIQUES 6
Informed Search - Greedy Best First - A* algorithm - Adversarial Game and Search -
Game theory - Optimal decisions in game - Min Max Search algorithm - Alpha-beta
pruning – Constraint Satisfaction Problems (CSP) - Examples - Map Coloring - Job
Scheduling - Backtracking Search for CSP.
3 UNIT III
LEARNING 6
Machine Learning: Definitions – Classification - Regression - approaches of machine
learning models - Types of learning - Probability - Basics - Linear Algebra – Hypothesis
space and inductive bias, Evaluation. Training and test sets, cross validation, Concept of
over fitting, under fitting, Bias and Variance - Regression: Linear Regression - Logistic
Regression.
4 UNIT IV
SUPERVISED LEARNING 6
Neural Network: Introduction, Perceptron Networks – Adaline - Back propagation
networks - Decision Tree: Entropy – Information gain - Gini Impurity - classification
algorithm - Rule based Classification - Naïve Bayesian classification - Support Vector

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Machines (SVM).
5 UNIT V
UNSUPERVISED LEARNING 6
Unsupervised Learning – Principle Component Analysis - Neural Network: Fixed
Weight Competitive Nets - Kohonen Self-Organizing Feature Maps – Clustering:
Definition - Types of Clustering – Hierarchical clustering algorithms – k-means
algorithm.


Unit-I
INTELLIGENT AGENT AND UNINFORMED SEARCH
Two mark Question and Answers
1. 1. What is an Intelligent Agent?

An Intelligent Agent is an autonomous entity that perceives its environment, reasons about
the current state, and takes actions to achieve its goals.

2. What are the characteristics of an Intelligent Agent?

The characteristics of an Intelligent Agent include autonomy, reactivity, proactivity, and
social ability.

3. What is the nature of the environment in which an Intelligent Agent operates?

The environment can be classified as fully observable or partially observable, deterministic
or stochastic, episodic or sequential, static or dynamic, discrete or continuous, and single-
agent or multi-agent.

4. What is the structure of an Intelligent Agent?

The structure of an Intelligent Agent includes a performance element, a learning element, a
knowledge base, and an environment interface.

5. What is a Problem Solving Agent?

A Problem Solving Agent is a type of Intelligent Agent that solves problems by searching
for a sequence of actions that achieves its goal.

6. What are the steps involved in formulating a problem?

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The steps involved in formulating a problem include defining the initial state, goal state,
actions, and transition model.
7. What is Uninformed Search?

Uninformed Search is a type of search strategy that does not use problem-specific
knowledge to guide the search.

8. What are the types of Uninformed Search algorithms?

The types of Uninformed Search algorithms include Breadth-First Search, Uniform-Cost
Search, Depth-First Search, and Depth-Limited Search.

9. What is Breadth-First Search?

Breadth-First Search is an Uninformed Search algorithm that explores all nodes at the
present depth before moving to the next depth level.

10. What is Dijkstra's algorithm or Uniform, and what is its use?

Dijkstra's algorithm or Uniform-Cost Search is an Uninformed Search algorithm that uses a
priority queue to select the next node to expand, where the priority is the cost of reaching
the node.

11. What is Depth-First Search?

Depth-First Search is an Uninformed Search algorithm that explores as far as possible along
each branch before backtracking.

12. What is Depth-Limited Search?

Depth-Limited Search is an Uninformed Search algorithm that is similar to Depth-First
Search, but with a limit on the depth of the search.

Important Concepts:

- Frontier: The set of nodes to be explored next.
- Explored: The set of nodes that have been explored.
- Path Cost: The cost of reaching a node from the initial state.
- Optimal Solution: The solution with the minimum path cost.

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Algorithms:

- Breadth-First Search (BFS)
- Explore all nodes at the present depth before moving to the next depth level.
- Use a queue to implement BFS.
- Uniform-Cost Search (UCS)
- Use a priority queue to select the next node to expand, where the priority is the cost of
reaching the node.
- Explore nodes in order of increasing path cost.
- Depth-First Search (DFS)
- Explore as far as possible along each branch before backtracking.
- Use a stack to implement DFS.
- Depth-Limited Search (DLS)
- Similar to DFS, but with a limit on the depth of the search.
- Use a stack to implement DLS.
13 and 15 Marks QA
2. What is an Intelligent Agent? Explain its characteristics.




What is an Intelligent Agent?