OCS351 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FUNDAMENTALS L T P C
202 3
OBJECTIVES:
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.
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 : 30 PERIODS
PRACTICAL EXERCISES: 30 PERIODS
Programs for Problem solving with Search
1. Implement breadth first search
2. Implement depth first search
3. Analysis of breadth first and depth first search in terms of time and space
4. Implement and compare Greedy and A* algorithms.
Supervised learning
5. Implement the non-parametric locally weighted regression algorithm in order to fit
data points. Select appropriate data set for your experiment and draw graphs
6. Write a program to demonstrate the working of the decision tree based algorithm.
7. Build an artificial neural network by implementing the back propagation algorithm
and test the same using appropriate data sets.
8. Write a program to implement the naïve Bayesian classifier.
202 3
OBJECTIVES:
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.
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 : 30 PERIODS
PRACTICAL EXERCISES: 30 PERIODS
Programs for Problem solving with Search
1. Implement breadth first search
2. Implement depth first search
3. Analysis of breadth first and depth first search in terms of time and space
4. Implement and compare Greedy and A* algorithms.
Supervised learning
5. Implement the non-parametric locally weighted regression algorithm in order to fit
data points. Select appropriate data set for your experiment and draw graphs
6. Write a program to demonstrate the working of the decision tree based algorithm.
7. Build an artificial neural network by implementing the back propagation algorithm
and test the same using appropriate data sets.
8. Write a program to implement the naïve Bayesian classifier.
