Concepts and Algorithms of AI & ML

B.Tech Course Scheme

• B.Tech EXTC Sem 6 (Honours) [ Scheme ]

• AI and Machine Learning [ UG Syllabus PDF ]

Course Materials (PPTs/PDFs/Handouts):

1. Introduction

• • • Introduction to Intelligent Agents [ LaTeX PDF ]

      • PEAS in Artificial Intelligence [ LaTeX PDF ]

    • Introduction to Machine Learning [ PDF ]

      • Steps involved in developing a Machine Learning Application [ Google Colab  ]

      • Training Dataset vs Testing Dataset vs Validation Dataset [ Goolge Colab ]

      • Evaluation of Learning Algorithms [ PDF ]

        • Model Evaluation (Regularization) - 25 Jan 2025 [ PDF ]

        • Model Improvement (Ensemble Learning) - 25 Jan 2025 [ PDF ]

        • Model Optimization - 25 Jan 2025 [ PDF ]

        • Confusion Matrix  - Jan 2025 [ PDF ]  [ Spreadsheet ]

2. Introduction to AI Problems

   • Introduction to AI Problems [ LaTeX PDF ]

   • The Turing Test  [ LaTeX PDF ]

3. Solving Problems in AI: Searching Algorithms as Applied in AI

   • Uninformed / BlindSearch Algorithms [ PDF ] 

     1. BFS [ PDF ] 

     2. DFS [ PDF ] 

     3. Depth First with Iterative Deepening [ PDF ] 

   • Informed / Heuristic / Directed Search Algorithms: Part 1 [ PDF ]

     1. Generate and Test Search Algorithm [ PDF ]

     2. Simple Hill Climbing Algorithm [ PDF ]

     3. Steepest-Ascent Hill Climbing [ PDF ]

     4. Problems in Hill Climbing [ PDF ]

     5. Greedy Nearest Neighbor [ PDF ]

   • Informed / Heuristic / Directed Search Algorithms: Part 2 [ PDF ]

     1. Best First Search [ PDF ]

     2. Greedy Best First Search [ PDF ]

     3. Beam Search algorithm [ PDF ]

     4. A* search algorithm [ PDF ]

     5. AO* Search Algorithm  [ PDF ]

   • Adversarial Search Algorithms [ PDF ]

     1. The Minimax Algorithm [ PDF ]

     2. Alpha-Beta Pruning Algorithm [ PDF ]

4. Knowledge Representation and Reasoning

5. Preparing to model: Data Visualization and Preprocessing

6. Modelling and Evaluation

7. Supervised Learning

8. Supervised Learning: Classification

9. Unsupervised Learning: Clustering

Lab Experiments

• Guidelines for writing DL Lab Experiments - Jan 2023 [ PDF ]

• Intelligent Agents

• Introduction to AI Problems

• Solving Problems in AI: Searching Algorithms as Applied in AI

• Knowledge Representation and Reasoning

• Preparing to model: Data Visualization and Preprocessing

• Modelling and Evaluation

• Supervised Learning

• Supervised Learning: Classification

• Unsupervised Learning: Clustering

Lab Manual

• Sample Gray Scale 8-bit BMP Images for experiment [ IMAGE FOLDER ]

• Guidelines for writing Lab Experiments - Jan 2023 [ PDF ]

• AIML using Python = Lab Manual - July 2020 [ PDF ]

Course Examination and Evaluation

• 2024-25

  • Internal Assessments

    • IA-1: [ IA-1 QP PDF ]        [ IA-1 Solution PDF ]

    • IA-2: [ IA-2 QP PDF ]        [ IA-2 Solution PDF ]

  • Assignments: 

    • Assignment #1: QP PDF     [ Solution PDF ] 

    • Assignment #2: QP PDF     [ Solution PDF ]

  • Theory Examination

    • Theory QP PDF    [ TH Solution PDF ]  

Old Course Materials (PPTs/PDFs/Handouts)