AI and Data Analytics

UG Course Scheme

• B.E Mech Honors Sem 7 [ Scheme ]

• HRC701 Artificial Intelligence and Data Analytics [ Syllabus ]

• Question Bank [ PDF ]

Course Materials (PPTs/PDFs/Handouts):

• • Module 1

    • Introduction to Artificial Intelligence (AI)

    • Knowledge and Reasoning

  • Module 2

    • Planning in AI [ PDF ]

    • Reasoning Under Uncertainty [ PDF ]

    • Introduction to Expert Systems [ PDF ]

  • Module 3

    • Introduction to Data Mining [ PDF ]

    • Introduction to Big Data [ PDF ]

    • Introduction to Business Intelligence [ PDF ]

  • Module 4

    • Data Pre-processing [ PDF ]

    • Data Modeling and visualization [ PDF ]

    • Data Warehousing [ PDF ]

  • Module 5

    • Machine Learning [ PDF ]

    • Performance Measures [ PDF ]

  • Module 6

    • Classification [ PDF ]

      • SC Classification Techniques [ PDF ]

      • Classification Algorithms [ PDF ]

      • Decision Tree - Classification [ PDF ]

      • Decision Tree - Regression [ PDF ]

      • SVM Classification Example [ PDF ]

      • Vsat Introduction Classification - Naive Bayesian [ PDF ]

    • Clustering [ PDF ]

      • Clustering Algorithm [ PDF ]

      • Expectation-Maximization  EM Algorithm [ PDF ]

    • Dimensionality Reduction [ PDF ]

      • Singular Value Decomposition SVD Example [ PDF ]

      • Independent Components Analysis [ PDF ]

    • Time Series Analysis and Forecasting [ PDF ]

Course Examination and Evaluation

• 2023-24

  • Internal Assessment #1: [ PDF ]  [ Solution PDF ]

  • Internal Assessment #2: [ IA1 QP PDF ]  [ IA1 Solution PDF ]

  • Assignment #1 : [ Assign1 Question PDF ]       [ Assign1 Solution PDF ] 

  • Assignment #2:  [ Assign2 Question ]      [ Assign2 Solution ]  

  • Practice Quiz #1 [ Google Form ]

  • Theory Examination [ QP PDF ]   [ TH QP Solution PDF ]

• 2022-23

Lab Experiments

• Guidelines for writing Lab Experiments [ PDF ]

• Language: C, C++ uisng DevC

• Language: Python using Google Colab

  • Lab 1: Image Negarive [ PDF ]

    • InputImage [ giraffe.bmp ]

    • ImageNegativeCode [ .cpp ]

  • Lab 2: ImageThresholding [ PDF ]

    • InputImage [ bird.bmp ]

    • ImageThresholdingCode [ .cpp ]

  • Lab 3: [ PDF ]

    • InputImage [ coins.bmp ]

    • ImageSegmentationSobel [ .cpp ]

  • Lab 4: [ PDF ]

    • InputImage [ cat.bmp ]

    • ImageSegmentationPrewitt [ .cpp ]

  • Lab 5: [ PDF ]

    • To understand AND, OR, NAND and XOR using ANN [ PDF ]

• Lab 6: [ PDF ]

  • • Learning AND using ANN [ PDF ]

• Lab 7:  [ PDF ]

  • • To implement AND learning using Python  [ PDF ]

• • Lab 8: [ PDF ]

    • Forward and Inverse kinematics [ PDF ]

  • Lab 9: [ PDF ]

  • Lab 10: 

  • Lab 11: 

Lab Manual

• Lab Manual [ PDF ]

• Image Processing using C and C++ [ PDF ]