Introduction to Artificial Intelligence and Data Science

Sl. No.

                                            Contents                       

Contact Hours

1

Unit 01:

Data Science History, Data Science and Related Terminologies, Types of Analytics, Applications of Data Science, Data Science Process Models.

Introduction to AI, History and Foundation of AI, Intelligence, and it’s type,

Categorization of Artificial Intelligent based System, Agents & Environments, Applications, and Current trends in AI

 

10

2

Unit 02:

Introduction to Data, Types, Data Preprocessing, Understanding Data Requirements, Dealing with Erroneous/Missing Values, Standardizing Data, Steps involved in EDA using Python Programming/R.

Knowledge and Reasoning in AI: Knowledge based Agents, Syntax and Semantics, Forward Chaining, Backward Chaining, Knowledge Engineering, Belief Network                                                  

10

3

Unit 3:

Introduction to Modelling Techniques, Supervised Learning Algorithms- Regression, Classification, and Unsupervised Learning Algorithms- Clustering, Association Rule Mining

Feature Selection, Dimensionality Reduction, Independent and Dependent Variables, Relationship between Variables: Correlation, Multicollinearity, Factor Analysis, Treatment of Outliers

10

4

Unit 4:

Problem Solving Agent, Formulating Problems, Example Problems, Uninformed Search Methods, Informed Search Method, Local Search Methods, Genetic algorithms, Adversarial Search

10

5

Unit 5:

Applications of Analytics in Healthcare, Applications of Analytics in Agriculture, Applications of Analytics in Business, Applications of Analytics in Sports, Forms of Learning, Introduction to Expert Systems, Expert System Architecture, Capstone Project

8

 

Total

48