OCS351 Artificial Intelligence and Machine Learning Fundamentals Syllabus:

OCS351 Artificial Intelligence and Machine Learning Fundamentals Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

The main objectives of this course are to:
 Understand the importance, principles, and search methods of AI
 Provide knowledge on predicate logic and Prolog.
 Introduce machine learning fundamentals
 Study of supervised learning algorithms.
 Study about unsupervised learning algorithms.

UNIT I INTELLIGENT AGENT AND UNINFORMED SEARCH

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

UNIT II PROBLEM SOLVING WITH SEARCH TECHNIQUES

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

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

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

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.
Unsupervised learning
9. Implementing neural network using self-organizing maps
10. Implementing k-Means algorithm to cluster a set of data.
11. Implementing hierarchical clustering algorithm.
Note:
 Installation of gnu-prolog, Study of Prolog (gnu-prolog).
 The programs can be implemented in using C++/JAVA/ Python or appropriate tools can be used by designing good user interface
 Data sets can be taken from standard repositories
(https://archive.ics.uci.edu/ml/datasets.html) or constructed by the students.

COURSE 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

TOTAL : 60 PERIODS
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,

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.