Topic outline

  • Welcome to the world of Artificial Intelligence


    • Instructor Name:           Mostak Ahmad

      Designation:                   Lecturer

      Email:                              mostak.cse@diu.edu.bd

      Office Address:              

    • 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.

    • Course Objectives:

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


    • 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.

    • Assessment:

       Class Attendance

                     07

       Assignment

                     5

       Class Test

                     15

      Presentation

                       8

       Mid Term Exam

                    25

       Semester Final Exam

                    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

    • Text Book File

      Artificial Intelligence: A Modern Approach

      - Stuart Russel & Peter Norvig

      Edition: 3rd

    • Google Classroom Code:

  • Week 1

    • 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.

    • 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 and future scopes of AI


    • Class Recordings - Section:[D]
                  Introductory Session
                 Lecture 1

      Restricted Not available unless: You belong to Section D
    • Class Recordings - Section:[E]
                  Introductory Session
                 Lecture 1
      Restricted Not available unless: You belong to Section E

  • Week 2

    • 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

    • List out domain names of AI and submit it here within [27 Oct 2021] Assignment
      Restricted Not available unless: You belong to Section E
    • Class Recordings - Section:[D]
                  Lecture 2 : Part 1
                 Lecture 2 : Part 2
      Restricted Not available unless: You belong to Section D
    • Class Recordings - Section:[E]
                  Lecture 2 : Part 1
                 Lecture 2 : Part 2
      Restricted Not available unless: You belong to Section E

  • Week 3


    • 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

    • Class Recordings - Section:[D]
                  Lecture 3
      Restricted Not available unless: You belong to Section D
    • Class Recordings - Section:[E]
                  Lecture 3
      Restricted Not available unless: You belong to Section E

  • Week 4

  • Midterm Exam

    • Midterm Fall 2021

    • Date: 17th November 2021 
      Wednesday 
      Time: 9.00 a.m. to 11.30 a.m.

      Examination Rules:
      1.  Be ensure your account clearance first.
      1.  Exam will be started at 9.00 a.m
      1. You have to complete your answer and submit it within 11.30 a.m 
      1. You should manage back up of your resources like mobile charge, power bank, mobile data, account balance and others to avoid internet , electricity and other problem .
      1. You have to write your name, id, course code, section, date, teacher initial in your answer script.  

      Instructions: 

      • Save your exam script as pdf using the following format: CSE315-Section-ID-mid.pdf [Example: CSE315-D-192-15-10000-mid.pdf]
      • Upload the pdf in the google form / BLC assignment slot
      • Plagiarism is a crime. Avoid plagiarism
      • Use your student email address to access all links
      ***** You can submit your answer script through BLC or Google form in time. 
      Can Scanner Mobile Application Link

    • Midterm answer Script Submission slot -Section : D Assignment
      Restricted Not available unless: You belong to Section D
    • Midterm answer Script Submission slot ; Section: E Assignment
      Restricted Not available unless: You belong to Section E

    • Google form Submission slot (if needed) ; Section : D

      (If you face any problem to submit on BLC, you can use this link)

      Restricted Not available unless:
      • You belong to Section D
      • It is before 17 November 2021, 11:30 AM

    • Google form Submission slot (if needed) ; Section: E

      (If you face any problem to submit on BLC, you can use this link)

      Restricted Not available unless:
      • You belong to Section E
      • It is before 17 November 2021, 11:30 AM

  • Week 5

  • Week 6

  • Week 8


    • 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

    • 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

  • Week 9

    • 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

    • 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

  • Week 10

  • 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


  • Week 12

  • Week 13

  • Week 14

    Final Exam

    Date and schedule of the exam will be announced later.

    • Topic 15

      • Topic 16

        • Topic 17

          • Topic 18

            • Topic 19