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Tribhuvan university CSIT 2074 Syllabus Artificial Intelligence

Artificial Intelligence - Syllabus

Course Overview and Structure
Syllabus Notes Old Questions Unit Wise Questions Question Bank

Embark on a profound academic exploration as you delve into the Artificial Intelligence course (AI) within the distinguished Tribhuvan university's CSIT department. Aligned with the 2074 Syllabus, this course (CSC261) seamlessly merges theoretical frameworks with practical sessions, ensuring a comprehensive understanding of the subject. Rigorous assessment based on a 60 + 20 + 20 marks system, coupled with a challenging passing threshold of , propels students to strive for excellence, fostering a deeper grasp of the course content.

This 3 credit-hour journey unfolds as a holistic learning experience, bridging theory and application. Beyond theoretical comprehension, students actively engage in practical sessions, acquiring valuable skills for real-world scenarios. Immerse yourself in this well-structured course, where each element, from the course description to interactive sessions, is meticulously crafted to shape a well-rounded and insightful academic experience.


Course Description: The course introduces the ideas and techniques underlying the principles and

design of artificial intelligent systems. The course covers the basics and applications of AI

including: design of intelligent agents, problem solving, searching, knowledge representation

systems, probabilistic reasoning, neural networks, machine learning and natural language

processing.


Course Objectives: The main objective of the course is to introduce concepts of Artificial

Intelligence. The general objectives are to learn about computer systems that exhibit intelligent

behavior, design intelligent agents, identify AI problems and solve the problems, design knowledge

representation and expert systems, design neural networks for solving problems, identify different

machine learning paradigms and identify their practical applications.


Units

Key Topics

  • Introduction to E-commerce
    IN-1

    Overview of E-commerce and its significance in the digital age.

  • E-business vs E-commerce
    IN-2

    Understanding the differences between E-business and E-commerce.

  • Features of E-commerce
    IN-3

    Key characteristics and benefits of E-commerce.

  • Pure vs Partial E-commerce
    IN-4

    Types of E-commerce models and their applications.

Key Topics

  • Introduction to E-commerce
    IN-1

    Overview of E-commerce and its significance in the digital age.

  • E-business vs E-commerce
    IN-2

    Understanding the differences between E-business and E-commerce.

  • Features of E-commerce
    IN-3

    Key characteristics and benefits of E-commerce.

  • Pure vs Partial E-commerce
    IN-4

    Types of E-commerce models and their applications.

Key Topics

  • Threads
    PR-3.1

    Threads are a way to achieve concurrency in a program, allowing multiple flows of execution to run concurrently within a single process.

  • Virtualization
    PR-3.2

    Virtualization is a technology that allows multiple virtual machines to run on a single physical machine, improving resource utilization and isolation.

  • Clients
    PR-3.3

    Clients are entities that request services or resources from a server, often in a distributed system or network.

  • Servers
    PR-3.4

    Servers are entities that provide services or resources to clients, often in a distributed system or network.

  • Code Migration
    PR-3.5

    Code migration refers to the process of moving code from one environment or system to another, often in a distributed system or cloud computing context.

  • Constraint Satisfaction Problems
    PR-3.6

    Solving problems with constraints, including techniques for finding solutions that satisfy all constraints.

Key Topics

  • Introduction to Knowledge Management
    KN-1

    This topic provides an overview of knowledge management, its importance, and its relevance in organizations.

  • Organizational Learning and Transformation
    KN-2

    This topic explores the role of knowledge management in organizational learning and transformation, and how it can lead to improved performance and competitiveness.

  • Knowledge Management Initiatives
    KN-3

    This topic discusses various initiatives that organizations can undertake to manage knowledge effectively, including knowledge sharing, documentation, and innovation.

  • Approaches to Knowledge Management
    KN-4

    This topic presents different approaches to knowledge management, including codification, personalization, and hybrid approaches.

  • Information Technology in Knowledge Management
    KN-5

    This topic examines the role of information technology in supporting knowledge management, including the use of knowledge management systems, artificial intelligence, and other digital tools.

  • Knowledge Management Systems Implementation
    KN-6

    This topic provides guidance on implementing knowledge management systems, including the design, development, and deployment of such systems.

Key Topics

  • Introduction to Matrices
    MA-1

    This topic introduces the concept of matrices, including their definition, notation, and basic operations. It lays the foundation for further study of matrices and their applications.

  • Types of Matrices
    MA-2

    This topic covers the different types of matrices, including square matrices, diagonal matrices, identity matrices, and zero matrices. It explains their properties and characteristics.

  • Equality of Matrices
    MA-3

    This topic defines and explains the concept of equality of matrices, including the conditions for two matrices to be equal and the rules for comparing matrices.

  • Algebra of Matrices
    MA-4

    This topic covers the basic algebraic operations of matrices, including addition, subtraction, multiplication, and scalar multiplication. It explains the rules and properties of these operations.

Key Topics

  • Sociology, Social Policy, and Social Planning
    AP-1

    This topic explores the relationship between sociology and social policy, and how sociological principles are applied in social planning to address social issues.

  • Social Problems
    AP-2

    This topic examines various social problems, including their causes, consequences, and potential solutions, using sociological theories and perspectives.

  • Data Warehousing and Data Mining in Agriculture
    AP-3

    Applications of data warehousing and data mining in the agriculture sector, including decision support systems and precision agriculture.

  • Data Warehousing and Data Mining in Rural Development
    AP-4

    Using data warehousing and data mining to support rural development initiatives, including poverty reduction and infrastructure planning.

Lab works

Laboratory Work Manual

Student should write programs and prepare lab sheet for most of the units in the syllabus. Majorly,

students should practice design and implementation of intelligent agents and expert systems,

searching techniques, knowledge representation systems and machine learning techniques. Students

are also advised to implement Neural Networks for solving practical problems of AI. Students are

advised to use LISP, PROLOG, and any other high level language like C, C++, Java, etc. The

nature of programming can be decided by the instructor and student as per their comfort. The

instructors have to prepare lab sheets for individual units covering the concept of the units as per the

requirement. The sample lab sessions can be as following descriptions;

Unit II: Intelligent Agents (4 Hrs)

- Write programs for implementing simple intelligent agents.

Unit III: Problem Solving by Searching (12 Hrs)

- Write programs for illustrating the concepts of

  •  Uninformed Search like DFS, BFS, etc.
  •  Informed Search like Greedy Best First, A*, etc.
  •  Game Search like MiniMax Search

- Write programs for constraint satisfaction problems like water jug, n-queen problem,

  cryptoarithmatic problem, etc.

Unit IV: Knowledge Representation (12 Hrs)

- Write programs for illustrating the concepts knowledge representation systems

  •  rule based (program with if then rules)
  •  predicate logic (using predicates like in Prolog)
  •  frames (using concepts of class)
  •  semantic nets (using concepts of graph)

Unit V: Machine Learning (10 Hrs)

- Write program for implementing Naive Bayes.

- Write program for implementing Neural Networks for realization of AND, OR gates.

- Write program for implementing Backpropagation Learning.

Unit VI: Applications of AI (7 Hrs)

- Write program for implementing expert systems like disease prediction, weather forecasting

  etc.

- Use library tools like NLTK to illustrate concepts of Natural Language Processing.