Topic outline

  • Artificial Intelligence Lab - Python


    •                             Instructor's Information


      Name: Md. Firoz Hasan

      Designation: Lecturer(Senior Scale)
      email: firoz.cse@diu.edu.bd
      Office address: Room#735, AB04, Level - 7

    • Course Content

      Introduction to Artificial Intelligence, AI Programming Fundamentals, Search Algorithms and Optimization, Knowledge Representation and Reasoning, Machine Learning and Data Analysis, Deep Learning and Neural Networks, and Natural Language Processing.

    • Course Rationale

      The Artificial Intelligence Lab is an immersive course designed to provide students with hands-on experience in implementing and applying artificial intelligence techniques. Through a combination of lectures, practical exercises, and project work, students will develop a deep understanding of AI concepts and gain proficiency in programming AI algorithms. The course focuses on both theoretical foundations and practical implementation aspects of AI, preparing students for real-world AI applications.


    • Course Objectives:

      By the end of the course, students will be able to:

      ·         Understand fundamental concepts and techniques in artificial intelligence.

      ·         Implement and evaluate AI algorithms and models.

      ·         Apply AI techniques to solve real-world problems in various domains.

      ·         Analyze and interpret data for AI applications.

      ·         Demonstrate proficiency in AI programming and software tools.

      ·         Collaborate effectively in a team-based AI project environment.

      ·         Communicate and present AI solutions and findings clearly.

    • Assessment Policies:

      Class Attendance

                    10

      Lab Performance

                     25

       Project

                     25

      Lab Final

                       40

       Total

      		100



    • Grading Policy:


      Numerical Grade

      Letter Grade

      Grade Point

      80% and above

      A+

      (A Plus)

      4.0

      75% to less than 80%

      A

      (A regular)

      3.75

      70% to less than 75%

      A-

      (A minus)

      3.5

      65% to less than 70%

      B+

      (B Plus)

      3.25

      60% to less than 65%

      B

      (B regular)

      3.0

      55% to less than 60%

      B-

      (B minus)

      2.75

      50% to less than 55%

      C+

      (C Plus)

      2.5

      45% to less than 50%

      C

      C (regular)

      2.25

      40% to less than 45%

      D

       

      2.0

      Less than 40%

      F

       

      0.0



  • Week 1

    Session I: Introduction to Data Visualization and Machine Learning Libraries

    Aim of the Session:

    –  Introduce data visualization libraries

    –  Hands-on work for data visualization using different datasets

    - Introduce Machine Learning Libraries


  • Week 2

    Session II: Data Processing

    Aim of the Session:

    –  Hands-on work with data 


  • Week 3

    Session III: Supervised ML Algorithms (Part 1)

    Aim of this lecture:

    –  Discuss Linear Regression

    –  Discuss Logistic Regression

    - Discuss KNN

    –  Apply Linear Regression, Logistic Regression, and KNN on Datasets 


  • Week 4

    Session IV: Supervised ML Algorithms (Part 2)

    Aim of this lecture:

    –  Discuss Linear Navie Byes

    -  Discuss Support Vector Machine

    - Discuss Decision Tree

    - Discuss Random Forest

    –  Apply NB, SVM, DT, and RF on Datasets 


    • Supervised Learning Algorithm 

       

       

  • Week 5

    Session V: Feature Engineering and Model Optimization

    Aim of the session:

    - Implement different Feature Engineering techniques on Data

    - Apply Optimization Techniques to improve the performance of an ML model


    • Feature Engineering and Model Optimization


       

       

    • Lab Report(Feature Engineering ) Assignment
      Opened: Sunday, 17 December 2023, 12:00 AM
      Due: Sunday, 24 December 2023, 12:00 AM

  • Week 6

    Session VI: Uninformed Search

    Aim of this lecture:

    –  Discussion on Uninformed Search (BFS, DFS, UCS)

    –  Apply uninformed search on a given problem


    • Lab Task Assignment
      Opened: Monday, 4 September 2023, 12:00 AM
      Due: Monday, 11 September 2023, 12:00 AM

    • Uninformed Search

       

       

  • Week 7

    Session VII: Uninformed Search

    Aim of this lecture:

    –  Discussion on Informed Search (Greedy, A*)

    –  Apply Informed search on a given problem


    • Uninformed Search

       

       

    • Lab Report(BFS,DFS) Assignment
      Opened: Sunday, 17 December 2023, 12:00 AM
      Due: Sunday, 24 December 2023, 12:00 AM

  • Week 8

    Session VIII: Local Search and Adversial Search

    Aim of the session:

    - Discussion on Local Search
    - Discussion Adversial search
    - ApplyLocal and Adversial search 

    • Local Search Algorithm

       

       

  • Week 9 & 10

    Midterm Exam

    • Week 11

      Session IX: Unsupervised Machine Learning

      Aim of this session:

      - Discuss Unsupervised Learning

      - Implement SOTA Unsupervised algorithms 

      • Unsupervised Learning Algorithm

         

         

    • Week 12

      Session X: Artificial Neural Network

      Aim of this lecture:

      –  Discussion on Neural Network
      –  Implementation of Neural Network on Datasets  


      •  

         

    • Week 13

      Session XI: Artificial Neural Network

      Aim of this lecture:

      –  Discussion on Neural Network
      –  Implementation of Neural Network on Datasets  

      •  

         

    • Week 14

      Session XII: Convolutional Neural Network

      Aim of this lecture:

      –  Discussion on Convolutional Neural Network
      –  Implementation of Convolutional Neural Networks on Datasets  


      •  

         

    • Week 15

      Session XIII: Recurrent Neural Network

      Aim of this lecture:

      –  Discussion on Recurrent Neural Network
      –  Implementation of Recurrent Neural Networks on Datasets  


      •  

         

    • Week 16

      Project Presentation and Lab Report Submission

      • Week 17

        Lab Final