Section outline

  • Welcome to the world of Artificial Intelligence

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    • Welcome Message for Artificial Intelligence

      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.

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      Instructor's Information

      Name: ABU KHALID MUBASHSHIR MAHMUD

      Designation: LECTURER
      email: khalid.cse@diu.edu.bd
      Office Address: Room no. 111, DT-5
      Contact number: 01878653709

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      Course Rationale:

      Artificial intelligence (AI) is a research field that studies how to realize the intelligent human behaviors on a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. Although AI has been studied for more than half a century, we still cannot make a computer that is as intelligent as a human in all aspects. In this course, we will study the most fundamental knowledge for understanding AI. We will introduce some basic search algorithms for problem solving; knowledge representation and reasoning; game playing theories; Uncertainty; natural language processing and neural networks.

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      Course Objectives:

      The main objective of this course is to provide an introduction to the basic principles and applications of artificial intelligence.


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      After Completing the course students will be able to:

      CO1: Understand the concepts of Artificial intelligence, Intelligent Agents And issues in the design of search programs. 

      CO 2: Explain the role of agents and how it is related to environment and the way of evaluating it and how agents can act by establishing goals.

      CO 3: Analyze and simulate various searching techniques, constraint satisfaction problem and example problems- game playing techniques.. 

      CO 4: Explain the concepts of Logical Agents, Uncertainty,Natural Language processing and Expert Systems.

      CO 5: Analyze and design a real world problem for implementation and understand the dynamic behavior of a system.

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      Assessment:

       Class Attendance

                     07

       Assignment

                     5

       Class Test

                     15

      Presentation

                       8

       Mid Term Exam

                    25

       Semester Final Exam

                    40

      Total:    

      100



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      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

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      Text Book:

      Artificial Intelligence: A Modern Approach

      - Stuart Russel & Peter Norvig

      Edition: 3rd


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      Resources

      All slides

      Text Book

  • Forum

  • Notice Board

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      Quiz-1

      Syllabus: Contents of week-1, week-2, and week-3

      For section I, Date: 2.3.21

      For section J, Date: 2.3.21

      For section K, Date: 2.3.21

      Time will be fixed after discussion with the CR.

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      Quiz 1 (I Section) Assignment

      The question and Google Form for submission can be found here.
      https://forms.gle/REgXRYMKQgszcUoA9

      Also, give your attendance in BLC

      Not available unless: You belong to I
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      Quiz 1 (J Section) Assignment

      The question and Google Form for submission can be found here.
      https://forms.gle/ZRT5V9U39PMhkWSi7

      Also, give your attendance in BLC

      Not available unless: You belong to J
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      Quiz 1 (K Section) Assignment

      The question and Google Form for submission can be found here.
      https://forms.gle/REgXRYMKQgszcUoA9

      Also, give your attendance in BLC

      Not available unless: You belong to K
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      Midterm Examinations Assignment
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      I section form: https://forms.gle/gjQptewuyRW2Cd8V6
      J section form: https://forms.gle/qR19vPadn8sXiXng9
      K section form: https://forms.gle/kCzzY6QiLW5EPWWt7

      The Question is also in the form.

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      Guidelines

      Time: 1:30 pm to 04:00 pm.
      Use the Response template for your answer.
      Submit both in BLC and in Google form
      Please submit handwritten scripts.
      Follow the naming conventions like before.
      If you have any issues or problems call me immediately.

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      Presentation Instructions

      The instructions for the presentations are as follows.

      1. Submit the slide that you are presenting.

      2. Submit the google meet recording like the google meet sessions.

      3. Turn on the camera while you are presenting.

      4. If you have any questions ask them in the telegram group.

      5. This is a single person presentation

      6. You can choose to present any topic

      7. At least present 10 slides(without the introductory or conclusion slides)

      8. The last date of submission is 10.04.20(11:59 pm)

      9. This deadline will be strictly maintained, please submit as early as possible.


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      Presentation Submission Assignment
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      Follow the presentation instructions and submit the materials here.

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      Final Assessment Assignment
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      Submit your Google Drive link.

      If you have any problem call me immediately.

      Submit handwritten scripts.

      Here is the Question

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      Class Assessment Quiz
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      Participate in the Class Assessment 

      Access time: 4:30pm to 7:45pm

      Duration: 35 min.

      No. of attempts: 02
      Before you attempt the second time, call me for permission.

  • Week 1

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      Lesson 1: Introducing Class

      Formal introduction between students and teacher. Outline of the course will be shared and course related topic will be discussed in order to make students familiar about the outcome of the course.

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      Lesson 2:

      Topic Name: Introduction to AI

      After completing the lesson students will be able to:

      • Understand the fundamental ideas of AI
      • Understand the foundational concepts of AI
      • Know about the initial experiments and future scopes of AI

      Slide
      Lecture-1

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      Slide-1 File
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    • Class Records

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      Here is the link

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    • Class Records

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      Here is the link

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  • Week 2

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      Lesson 3 and 4:

      Learning Objectives

      After completing the lecture students will be able to:

      • Explain about agent and rational agents
      • Define PEAS for any given agent
      • Explain about different kinds of Agents' Environments 
      • Differentiate between different kinds of agents

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      Lecture Slide

      The Lecture slide is here.

      Tutorial 

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  • Week 3


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      Lesson 5 and 6:

      Learning Objectives:

      After completeing this lecture students will be able to:

      • Describe the properties of Espert Systems
      • Explain different aspects of Expert Systems

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      Lecture Slide

      The lecture slide is here

      Tutorial


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    • Class records

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    • Class records

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  • Week 4

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      Lesson 7 and 8:

      Learning Objectives:

      After completing this lecture students will be able to:

      • Formulate AI program
      • Apply Uninformed search algorithms on given problems

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      Lecture Slide

      The slide is here

      Tutorial

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  • Week 5

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      Lesson 9:

      Continuation of topic 4 (Problem solving by Search).

      Lecture Slide

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      Lesson 10:

      Learning Objectives:

      After completing this lecture students will be able to:

      - Explain Heuristic functions for some toy problems

      - Simulate Greedy search on a given problem

      Lecture Slide

      Tutorial


    • Class records

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  • Week 6

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      Lesson 11:

      Learning Objectives:

      After completing this lecture students will be able to:

      - Explain Heuristic functions for some toy problems

      - Simulate and apply A* search on a given problem

      Lecture Slide

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      Tutorial


    • Class records

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    • Class records

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      Not available unless: You belong to J

    • Class records

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      Not available unless: You belong to K
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      Lesson 12:

      Review on midterm exam topics

  • Week 8


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      Lesson 13:

      Learning Objectives:

      After completing this lecture students will be able to:

      - Explain about Hill-Climbing Search

      - Explain about Local Search

      - Apply Local search on a given problem


      Slides

      Slide Lecture

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      Lesson 14:

      Learning Obectives:

      After completing this lecture students will be able to:

      • Evaluate utility funcs of some basic game playing algorithms
      • Apply greedy and minimax algorithm on some popular turn taking games
      Slide

      Slide Lecture


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      Tutorial

    • Class Recording

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    • Class Recording

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    • Class Recording

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  • Week 9

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      Lesson 15:

      Learning Obectives:

      After completing this lecture students will be able to:

      • Evaluate utility funcs of some basic game playing algorithms
      • Simulate greedy and minimax algorithm for some popular turn taking games

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      Lesson 16: 

      Topic: Logical Agent

      Learning Objectives:

      After completing this students will be able to:

      - Explain about Logical agents

      - Apply First Order Propositional Logic to infer new knowledge

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      Slide 

      Here is the slide

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      Tutorial


    • Class Recording

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    • Class Recording

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    • Class Recording

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  • Week 10

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      Lesson 17: 

      Topic: Logical Agent

      Learning Objectives:

      After completing this students will be able to:

      - Explain about Logical agents

      - Apply First Order Propositional Logic to infer new knowledge


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      Lesson 18:

      Topic: Uncertainty

      Learning Objectives:

      After Completing this lecture students will be able to:

      - Explain uncertain features of real world

      - Explain Kolmogorov's axioms

      - Apply Bayes Theorem to take a decision in some uncertainty based problems

      Lecture Slide

      The slide is here

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      Tutorials


    • Class Recording

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    • Class Recording

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    • Class Recording

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  • Week 11

    Lesson 19:

    Topic: Uncertainty

    Learning Objectives:

    After Completing this lecture students will be able to:

    - Explain about uncertain features of real world

    - Explain Kolmagorov's axioms

    - Apply Bayes Theorem to take decision in some uncertainty based problems


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      Tutorials


    • Class Recording

      Here is the link

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    • Class Recording

      Here is the link

      Not available unless: You belong to J

    • Class Recording

      Here is the link

      Not available unless: You belong to K
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      Lesson 20

      Topic: Natural Language Processing

      Learning Objectives:

      After Completing this lecture Students will be able to:

      - Explain formal language and grammar

      - Identify whether any given string follow any formal grammar or not

      Tutorial


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      presentation Submission

  • Week 12

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      Lesson 21

      Topic: Natural Language Processing

      Learning Objectives:

      After Completing this lecture Students will be able to:

      - Explain formal language and grammar

      - Identify whether any given string follow any formal grammar or not

      Tutorial




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      Lesson 22

      Topic: Neural Network

      Learning Objectives:

      After Completing this lecture students will be able to:

      - Explain fundamental ideas about Neural Network

      - Explain threshold and activation function


      Tutorials


  • Week 13

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      Lesson 23

      Topic: Neural network

      Learning Objectives:

      After completing this lecture students will be able to:

      - Explain the architecture of the Neural network

      Tutorial


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      Lesson 24

      Topic: Review of final Exam contents