Topic outline

  • Artificial Intelligence Lab - Python


    • Welcome Message:

      Are you wondering about future world? Yes, of course Artificial Intelligence is going to rule in industries all over the world. Not only that, it will play its role in household works also. An this course aims to introduce yourself with world of Artificial Intelligence. The focus of this course will include Intelligent Agent, Solving problems by searching, Game playing, Logical agents, Uncertainty, Natural language processing and neural network. These will be some of the essential fundamental knowledge for every computer scientists in future.

      You are welcome to browse the page.

      Hope we will have a memorable journey together.


    • Instructor's Information

      Name:  Fabliha Haque

      Designation: Lecturer
      email: fabliha.cse@diu.edu.bd
      Office address:  102/1, Sukrabad Mirpur Rd, Dhaka 1207  


    • Course Objectives:

      After completing the course students will be able to:

      - Explain fundamentals of Python Language

      - Solve real life problems using Python

      - Implement a few basic ML algorithms

    • 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



    • Reference Book:

    • Google meet class link:  https://meet.google.com/nmw-vijv-guk

  • Forum

  • Week 1

    • Session I: Basics of Python

      Intended Learning Outcome:
      At the end of the session, student should be able to:

      • Give some simple examples of Prolog programs

      • Discuss the three basics of Python

      – Syntax

      – Variables and data types

      – List, tuples, dictionaries

      - Conditions and Loops

      Resouces of learning:

      • lecture video

  • Week 2

    • Session II: Basics of Python

      Aim of this leacture:

        • The following topics will be discussed in the class

      – Condition and Loops

      - Function

      - Class and Objects


    • lecture video

  • Week 3

    • Session III: Data Processing

      Aim of the Session:

      –  Introduce NumPy and Panda
      –  Hands on work with data using NumPy or Panda

  • Week 4

    • Session IV: Data Visualization

      Aim of the Session:

      –  Introduce data visualization libraries
      –  Hands on work for data visualization using different datasets

  • Week 5

    • Session V: Machine Learning Algorithm (Linear and Logistic Regression)

      Aim of this lecture:

      –  Discuss on Linear Regression
      –  Discuss on Logistic Regression
      –  Apply Linear and Logistic Regression on Datasets  

  • Week 6

    • Session VI: Machine Learning Algorithm (KNN algorithm)

      Aim of this lecture:

      –  Discussion on KNN
      –  Apply KNN on Datasets  


  • Week 7

    Midterm Exam

    date and time will announced later.

    • Week 8

      • Session VII: Machine Learning Algorithm (Naive Bayes)

        Aim of this lecture:

        –  Discussion on Baive Bayes
        –  Apply Naive Bayes on Datasets  


    • Week 9

      • Session VIII: Machine Learning Algorithm (Support Vector Machine)

        Aim of this lecture:

        –  Discussion on SVM
        –  Apply SVM on Datasets  


    • Week 10

      • Session IX: Machine Learning Algorithm (Neural Network)

        Aim of this lecture:

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


    • Week 11

      • Session X: Machine Learning Algorithm (Neural Network - 2)

        Aim of this lecture:

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



    • Quiz

    • Performance Test

      • Week 12

        Project Presentation

      • Week 14

        Final Exam

        date and time will announced later.

      • Topic 17

        • Topic 18

          • Topic 19

            • Topic 20