Topic outline

  • Welcome to Artificial intelligence & Artificial intelligence Lab Class

    • Welcome Letter

      Dear Students

      Welcome to the Artificial Intelligence (CSE 412) & Artificial Intelligence Lab (CSE 412L) courses, I, Dr. Fizar Ahmed will be your co-pilot in this online journey of learning.

      I care about your success in these courses. I'm glad you are here.

      Dr. Fizar Ahmed
      Assistant Professor, Department of Computer Science and Engineering
      Daffodil International University





      Instructor


      Dr. Fizar Ahmed

      Assistant Professor, Department of CSE
      Daffodil International University

      https://sites.google.com

      Office: Room: 507, CSE Building

      Email: fizar.cse@diu.edu.bd

      Phone: +8801775695814




      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 Objective

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




      Course Outcomes (CO’s)

      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 environment and the way of evaluating it and how agents can act by establishing goals.
      CO3
      Analyze and simulate various searching techniques, constraint satisfaction problem 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.



      Text Books

      1.
      Artificial Intelligence A Modern Approach (3rd Edition)
      by Stuart Russell
      2.
      প্রোগ্রামিং ও পাইথন
      by Subeen



      Assessment Plan (Theory)

      Final Exam
      40
      Mid-term Exam
      25
      3 Class Tests
      15
      Attendance
      07
      Assignment
      05
      Presentation
      08
      Total
      100



      Assessment Plan (Lab)

      Attendance
      10
      Lab Performance
      25
      Lab Project
      25
      Lab Final
      40
      Total
      100


  • week 1 Introduction


  • week 2 Intelligent Agents


    • Topics of Discussion:

      • Agents and Environments
      • Good Behaviour: The Concept of Rationality
      • The Nature of Environments
      • The Structure of Agents


      Expected Learning Outcomes:

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

      Resources of Learning:


  • week 3 Expert systems


  • week 4 Solving problems by searching


  • week 5 Solving problems by searching (Continued)


  • week 6 Review of mid-term exam topics


    • Mid-term exam syllabus:

      • Introduction to AI (Week-1)
      • Intelligent agents (Week-2)
      • Expert systems (Week-3)
      • Solving problems by searching (Week-4 & Week-5)


      Discussion on Mid-term exam:

      • Discussion Video


    • Class Test Quiz

  • week 7 Mid-Term Examination


  • week 8 Informed search and exploration


    • Learning Objectives

      • Informed search strategies
      • Heuristic functions
      • Local search algorithms and optimization


      Resources of Learning:


  • week 9 Game Playing


    • Learning Objectives

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


      Resources of Learning:


  • week 10 Logical Agents


  • week 11 Review & Revisit all memories



  • Topic 13

    week 12 Final Examination


    • Final

    • End of the Semester.

  • Topic 14

    • Topic 15

      • Topic 16

        • Topic 17

          • Topic 18

            • Topic 19

              • Topic 20