A Level - Syllabus of Module: A9.5-R5 –Artificial Intelligence Concepts and
R Programming
Introduction :
Artificial Intelligence is the intelligence exhibited by machines or software. The
application areas of artificial intelligence are very vast and so this is a field of study
which is gaining importance day by day. This branch of engineering emphasizes on
creating intelligent machines that work and react like humans. There are different
dimensions for artificial intelligence, in which the decision taking capacity is most
important.
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| A Level - Syllabus of Module: A9.5-R5 –Artificial Intelligence Concepts and R Programming |
(i) Introduction To Artificial Intelligence : Introduction to Artificial Intelligence (AI), history of AI. Advantages of AI, need
for AI for modern applications, Intelligent agents, structure of Agents, agent
program: goal-based agents, utility-based agent, learning agents, agent environment, multi agent systems, components of intelligence. Foundations of AI
based Systems.
Introduction to Business Intelligence, Business Analytics, Data, Information, how
information hierarchy can be improved/introduced, understanding Business
Analytics, Introduction to OLAP, OLTP, data mining and data warehouse.
Difference between OLAP and OLTP. Use of AI in data analytics.
(ii) Applications of AI :
Applications of AI, health care sector, finance sector, smart cars, devices and homes,
travel and navigations, entertainment, security, automation, automobile industry.
(iii) Data Preparation and Machine Learning Basics :
Learning Systems. Supervised and Unsupervised Learning. Tasks performed by
Machine Learning Algorithms – Classification, Regression, Clustering, Association
rule Mining. Linear Regression, K-Nearest Neighbor Classifier, K-Means
Algorithm. Performance evaluation metrics of machine learning algorithmsAccuracy Score, Confusion Matrix, Root Mean Squared Error.
(iv) R Programming
R Programming: Basics - Vectors, Factors, Lists, Matrices, Arrays, Data Frames,
Reading data.
Data visualization –barplot, pie, scatterplot, histogram, scatter matrix.
(v) Statistical Data Analysis :
Statistical data analysis –Summary Statistics, Correlation and Regression,
Probability distributions- Normal distribution, Poisson distribution, Binomial
distribution
Types of data- Structured, Unstructured and Semi structured data.
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