### OCS351 Syllabus - Artificial Intelligence And Machine Learning Fundamentals - 2021 Regulation - Open Elective | Anna University

## OCS351 Syllabus - Artificial Intelligence And Machine Learning Fundamentals - 2021 Regulation - Open Elective | Anna University

OCS351 |
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS |
L T P C |
---|

**2023**

**OBJECTIVE:The main objectives of this course are to:**

1. Understand the importance, principles, and search methods of AI

2. Provide knowledge on predicate logic and Prolog.

3. Introduce machine learning fundamentals

4. Study of supervised learning algorithms.

5. Study about unsupervised learning algorithms.

2. Provide knowledge on predicate logic and Prolog.

3. Introduce machine learning fundamentals

4. Study of supervised learning algorithms.

5. Study about unsupervised learning algorithms.

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 uniform-cost search - Depth First Search - Depth Limited Search

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

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

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 Machines (SVM)

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

**TOTAL: 60 PERIODS**

**OUTCOMES:**

CO1: Understand the foundations of AI and the structure of Intelligent Agents

CO2: Use appropriate search algorithms for any AI problem

CO3: Study of learning methods

CO4: Solving problem using Supervised learning

CO5: Solving problem using Unsupervised learning

CO2: Use appropriate search algorithms for any AI problem

CO3: Study of learning methods

CO4: Solving problem using Supervised learning

CO5: Solving problem using Unsupervised learning

**TEXT BOOK:**

1. S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach”, Prentice Hall, Fourth Edition, 2021

2. S.N.Sivanandam and S.N.Deepa, Principles of soft computing-Wiley India.3 rd ed,

2. S.N.Sivanandam and S.N.Deepa, Principles of soft computing-Wiley India.3 rd ed,

**REFERENCES:**

1. Machine Learning. Tom Mitchell. First Edition, McGraw- Hill, 1997.

2. I. Bratko, “Prolog: Programming for Artificial Intelligence‖, Fourth edition, Addison-Wesley Educational Publishers Inc., 2011.

3. C. Muller & Sarah Alpaydin, Ethem. Introduction to machine learning. MIT press, 2020.

2. I. Bratko, “Prolog: Programming for Artificial Intelligence‖, Fourth edition, Addison-Wesley Educational Publishers Inc., 2011.

3. C. Muller & Sarah Alpaydin, Ethem. Introduction to machine learning. MIT press, 2020.

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