Topic outline

  • Artificial Intelligence



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    Lecturer

    Dept. of CSE

    firoz.cse@diu.edu.bd

    Message

    Hello My beloved Students

    Don't Afraid of Moving Forward, The Future is bright

    Keep Your Best Wishes and Your biggest goals close to your heart and dedicate time to them everyday. If yo truly care about what you do and your work diligently at it, there's almost nothing you can't Accomplish


    Course Details



    Course Objective


    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.


    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.

    CO1: Understand the concepts of Artificial intelligence, Intelligent Agents, and issues in the design of search programs. 
    CO2:  Explain the role of agents and how it is related to the environment and the way of evaluating it and how agents can act by establishing goals.
    CO3: Analyze and simulate various searching techniques, constraint satisfaction problems, and example problems- game playing techniques.. 
    CO4: Explain the concepts of Logical Agents, Uncertainty, Natural Language Processing, and Expert Systems.
    CO5: Analyze and design a real-world problem for implementation and understand the dynamic behavior of a system.

    DownLoad Course Outline

    Course Outline

    Attendance

    7%

    Class Test

    15%

    Assignment

    5%

    Presentation

    8%

    Mid Exam

    25%

    Final Exam

    40%
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    Academic Honesty

    1.Ensure Plagiarism <15%

    2.Reflect Self Understanding

    3.Avoid all unfair means to ensure growth of your positive mindset


    Textbook For CSE498

    • Artificial Intelligence: A Modern Approach 

      - Stuart Russel & Peter Norvig

      Edition: 3rd

    • Download


    Reference Book For CSE498


    •                 I.          “Artificial Intelligence,” 2nd edt., Elain Rich and Kevin Knight, Tata McGraw Hill.

                    II.          “Introduction to Artificial Intelligence and Expert Systems,” D. W. Patterson, Prentice Hall of India.

                  III.          “Artificial Intelligence: A Guide to Intelligent Systems,” 2nd edt., Michael Negnevitsky, Pearson Education Ltd.



    Counselling Time
    Saturday 10:00-11:20am;   1:00-2:20pm
    Sunday 10:00-1:00pm;     3:00-5:00pm;
    Monday 10:00-1:00pm;     3:00-5:00pm;
    Tuesday 10:00-1:00pm;     3:00-5:00pm;
    Wednesday 10:00-11:20am;   1:00-2:20pm
    Thursday 10:00-1:00pm;     3:00-5:00pm;


    Quick Access Components


    Navigation
    Class Test:  CT       Presentation        Mid Exam       Final Exam
    Week2Go:   WK1    WK2 WK3    WK4    WK5    WK6  WK7   WK8     WK9     WK10    WK11    WK12    WK13    WK14

  • Week 1

  • Week 2

  • Week 3


  • Week 4

  • Week 5

  • Week 6

    • 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


    • Lesson 12:

      Review on midterm exam topics

  • Week7: Miderm Exam

    Mid-Term Syllabus:

                                                                   Week 1: Introduction to AI
                                                                   Week 2: Intelligent Agent
                                                                   Week 3: Expert Systems
                                                                   Week 4:  Uninformed search algorithms
                                                         

    Midterm Marks Distribution:

    • Midterm Exam will be made up of 4 sets of questions. 
    • Each set of questions may include two or three short or descriptive questions. 
    • There will be no options. You must answer all the questions to get marks.

    Mid Exam Question,  Answer Script, and Submission Link



    Mid Exam Question  --------- Download Here


  • Week 8


  • Week 9

    • Lesson 14:


      Learning Objectives:

      After completing this lecture students will be able to:

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


  • Week 10

    • Lesson 15: 

      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


  • Week 11

    • Lecture 16:

      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 the decision in some uncertainty based problems





  • Week 12

    • Lesson 17:

      Topic: Machine Learning

      Learning Objectives:

      After Completing this lecture Students will be able to:

      - Supervised machine learning, Unsupervised ML

      - Confusion Matrix

  • Week 13

    • Lesson 18

      Topic: Uncertainty

      Learning Objectives:

      After completing this lecture students will be able to:

      - Learn Bayes Theorem

      -Conditional Probability

    • Lesson 19

      Topic: Review of final Exam contents

  • Final Exam

    Final Exam Syllabus:

    1. Informed search and exploration
    2.  Game-playing 
    3. Logical agents         

    Final Exam Guide Line

    Course Code: CSE315   Date: 31-08-2021 Time: 01:30pm-05:00pm

    • Download Final Answer Script Template (Word Document)[  DOC]

    • Write or Type in the Answer Script doc file (Your Answer)
    • Remember to save your work as you go along
    • When finished, save your word document as PDF
    • Check the size of your PDF before submitting your response (5MB or Less)
    • Upload your Exam Response here in this section