A Level: Artificial Intelligence Concepts and R Programming (A9.5-R5, NIELIT / DOEACC, LIVE Classes)



    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-making capacity is most important.

    At the end of the course, the students will be able to

    • Identify the scope and limits of the Artificial Intelligence (AI) field.
    • Analyze the application areas of Artificial Intelligence.
    • Explore data, process it, and make it ready for developing AI-based systems.
    • Apply R Programming for data preparation, data exploration& visualization
    • Apply Probability and Statistics for solving problems in real life.
    • Apply R programming tool to obtain results of statistical data analysis problems.

    120 Hours – (Theory: 48 hrs + Practical: 72 hrs)

    Detailed Syllabus

    (i) Introduction To Artificial Intelligence
    Introduction to Artificial Intelligence (AI), history of AI. Advantages of AI, need for AI for modern applications, Intelligent agents, the 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 algorithm accuracy 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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