BSc CSIT 6th Semester Syllabus | All Subject Syllabus BSc. CSIT 6th Sem
Download csit 6th-semester subject wise Syllabus:
Software Engineering
Course Description:
This course familiarizes students with different concepts of
software engineering mainly focusing on software process models, agile
development, requirements engineering, models, design, implementation, testing,
evolution, and software project management.
Course Objectives:
The main objective of this
course is to provide knowledge of different concepts of software engineering so
that students will be able to develop high quality software using different
software management skills.
Course Contents:
Unit 1: Introduction (2
Hrs.)
Software and its Types;
Attributes of Good Software; Software Engineering and its Importance;
Fundamental Software Engineering Activities; Difference between Software
Engineering and Computer Science; Difference between Software Engineering and
System Engineering; Challenges and Cost of Software Engineering; Professional
Software Development; Software Engineering Diversity; Internet Software
Engineering; Software Engineering Ethics
Unit 2: Software
Processes (5 Hrs.)
Software Process; Software
Process Models (Waterfall Model; Incremental Development; Integration and
Configuration); Software Process Activities (Software Specification, Software
Design and Implementation; Software Validation; Software Evolution); Coping
with Change (Prototyping, Incremental Delivery); Process Improvement
Unit 3: Agile Software
Development (3 Hrs.)
Agile Development; Plan-Driven vs. Agile Development; Agile
Methods; Agile
Development Techniques; Introduction to Agile Project
Management
Unit 4: Requirements
Engineering (3 Hrs.)
Concept of User and System Requirements; Functional and
Non-Functional Requirements;
Requirements Engineering Process; Requirements Elicitation;
Requirements Specification; Requirements Validation; Requirements Change
Unit 5: System Modeling
(6 Hrs.)
Introduction to System Modeling; Context Models; Interaction
Models; Structural Models; Behavioral Models; Model-Driven Architecture
Unit 6: Architectural
Design (6 Hrs.)
Introduction; Architectural Design Decisions; Architectural
Views; Architectural Patterns; Application Architectures
Unit 7: Design and
Implementation (5 Hrs.)
Introduction; Object-Oriented Design using UML; Design
Patterns; Implementation Issues; Open-Source Development
Unit 8: Software
Testing (5 Hrs.)
Introduction; Validation and Verification Testing; Software
Inspection; Software Testing Process; Development Testing; Test-Driven
Development; Release Testing; User Testing
Unit 9: Software
Evolution (3 Hrs.)
Evolution Process; Legacy Systems; Software Maintenance
Unit 10: Software
Management (7 Hrs.)
Software Project Management;
Project Management Activities (Project Planning, Risk Management, People
Management, Reporting and Proposal Writing); Project Planning (Software
Pricing, Plan-Driven Development, Project Scheduling, Estimation Techniques,
COCOMO Cost Modeling); Introduction to Quality Management and Configuration
Management
Laboratory / Project
Work:
Students should prepare a
project report along with software product using different concepts of software
engineering. The project can be done in groups with at most four members in
each group using any suitable database, programming, interfacing technologies,
and project management concepts.
Text Book:
1. Software Engineering, 10th Edition, Ian Sommerville,
Pearson Education 2016
References Books:
1.
Software Engineering: A Practitioner‟s Approach, 8th
Edition, Roger S. Pressman and Bruce R. Maxim, McGraw-Hill Education 2015
2.
Beginning Software Engineering, Rod Stephens, John
Wiley & Sons Inc 2015
Compiler Design and
Construction
Compiler Design and Construction
CSC365
Theory + Lab
Course Description:
This course is designed to
develop acquaintance with fundamental concepts of compiler design. The course
starts with the basic concepts and also includes different phases of compilers
like lexical analysis, syntax analysis, syntax-directed translation, type
checking etc. in detail.
Course Objectives:
•
To develop knowledge in compiler design
•
To develop lexical analyzers, parsers, and small
compilers using different tools
•
To develop lexical analyzers, parsers, and small
compilers by using general purpose programming languages.
Course Contents:
Unit 1: (3
hrs)
1.1 Compiler
Structure: Analysis and Synthesis Model of Compilation, different sub-phases
within analysis and synthesis phases
1.2 Basic
concepts related to Compiler such as interpreter, simple One-Pass Compiler,
preprocessor, macros, symbol table and error handler.
Unit 2: (22
hrs)
2.1 Lexical
Analysis: Its role, Specification and Recognition of tokens, Input Buffer,
Finite Automata relevant to compiler construction syntactic specification of
languages, Optimization of DFA based pattern matchers
2.2 Syntax
Analysis: Its role, Basic parsing techniques: Problem of Left Recursion, Left
Factoring, Ambiguous Grammar, Top-down parsing, Bottom-up parsing, LR parsing
2.3 Semantic
Analysis: Static & Dynamic Checks,
Typical Semantic errors, Scoping, Type Checking; Syntax directed definitions
(SDD) & Translation (SDT), Attribute Types: Synthesized & Inherited,
Annotated Parse Tree, S-attributed and L-attributed grammar, Applications of
syntax directed translation, Type Systems, Type Checking and Conversion
Unit 3: (4hrs)
3.1 Symbol
Table Design: Function of Symbol Table, Information provided by Symbol Table,
Attributes and Data Structures for symbol table
3.2 Run–time
storage management
Unit 4: (16
hrs)
4.1 Intermediate
Code Generator: High-level and Low-level Intermediate representation, Syntax
tree & DAG representations, Three-address code, Quadruples, Triples, SDT
for intermediate code, Intermediate code generation for Declarations,
Assignments, Control Flow, Boolean Expressions and Procedure Calls; Back
patching
4.2 Code
Generator: Factors affecting a code generator, Target Language, Basic blocks
and flow graphs, Dynamic programming code-generation algorithm
4.3 Code
Optimization: Need and criteria of Code Optimization, Basic optimization
techniques
4.4 Case
Studies of some compilers like C compiler, C++ complier
Laboratory Works:
The laboratory work develops practical knowledge on
different concepts of compiler design.
Students should
•
Create a project by using lexical analyzer
generator or any high level language
•
Create a parser by using parser generator or any
high level language
•
Write programs for intermediate code generation
and machine code generation
•
Create front end of a compiler and using general
purpose programming languages
Recommended Books:
1.
Compilers Principles, Techniques, and Tools, Alfred V.
Aho, Ravi Sethi, Jeffrey D. Ullman; Pearson Education
2.
Introduction to Automata Theory, Languages, and
Computation, Johne E. Hopcroft, Rajeev Motwani, Jeffrey D. Ulman, Pearson
Education
3.
Advanced Compiler Design and Implementation, Steven
Muchnick, Morgan Kaufman Publication
E-Governance
E-Governance
CSC366
Theory + Lab
Course Description:
This course familiarizes students with different concepts of
E-Government and E-Governance, different E-Governance models and infrastructure
development, E-government security, and data warehousing and data mining for
e-governance.
Course Objectives:
•
To develop knowledge of e-governance and
e-government
•
To know different e-governance models and
infrastructure development
•
To implement security and use data warehousing
and mining in e-governance
Course Detail:
Unit 1: Introduction to
E-Government and E-Governance (5 Hrs.)
Difference between E-Government
and E-Governance; E-Government as Information System; Benefits of E-Government;
E-Government Life Cycle; Online Service Delivery and Electronic Service
Delivery; Evolution, Scope and Content of E-Governance; Present Global Trends
of Growth in E-Governance
Unit 2: Models of
E-Governance (10 Hrs.)
Introduction; Model of Digital Governance: Broadcasting /
Wider Dissemination Model,
Critical Flow Model, Comparative Analysis Model,
Mobilization and Lobbying Model,
Interactive – Service Model /
Government-to-Citizen-to-Government Model (G2C2G); Evolution in E-Governance
and Maturity Models: Five Maturity
Levels; Characteristics of Maturity Levels; Towards Good Governance through
E-Governance Models
Unit 3: E-Government
Infrastructure Development (10 Hrs.)
Network Infrastructure; Computing Infrastructure; Data
centers; E-Government Architecture;
Interoperability Framework;
Cloud Governance; E-readiness; Data System Infrastructure; Legal Infrastructural
Preparedness; Institutional Infrastructural Preparedness; Human Infrastructural
Preparedness; Technological Infrastructural Preparedness
Unit 4: Security for e-Government (5 Hrs.)
Challenges and Approach of
E-government Security; Security Management Model; EGovernment Security
Architecture; Security Standards
Unit 5: Applications of
Data Warehousing and Data Mining in Government (5 Hrs.)
Introduction; National Data Warehouses: Census Data, Prices
of Essential Commodities;
Other Areas for Data Warehousing and Data Mining:
Agriculture, Rural Development, Health, Planning, Education, Commerce and
Trade, Other Sectors
Unit 6: Case Studies
(10 Hrs.)
E-Government Initiatives in Nepal,
Cyber Laws, Implementation in the Land Reform, Human Resource Management
Software, NICNET, Collectorate , Computer-aided Administration of Registration
Department (CARD), Smart Nagarpalika, National Reservoir Level and Capacity
Monitoring System, Computerization in Andra Pradesh, Ekal Seva Kendra,
Sachivalaya Vahini, Bhoomi, IT
in Judiciary, E-Khazana , DGFT, PRAJA, E-Seva, EPanchyat, General Information
Services of National Informatics, Centre E-Governance initiative in USA,
E-Governance in China, E-Governance in Brazil and Sri Lanka
Laboratory Work:
The laboratory work includes implementing e-governance
models and systems using suitable platform.
Text / Reference books:
1.
Richard Heeks, Implementing and managing e-Government
2. C.S.
R Prabhu, e-Governance: Concepts and Case studies, prentice hall of India Pvt.
Ltd.
3.
J. Satyanarayana, e-Government, , prentice hall of
India Pvt. Ltd
4.
Backus, Michiel, e-Governance in Developing Countries,
IICD Research Brief, No. 1, March 2001
NET Centric
Computing
NET Centric Computing
CSC367
Theory + Lab
Course Description:
The course covers the concepts of cross-platform web
application development using the ASP.NET Core MVC framework using C#
programming Language.
Course Objectives:
The objective of this course is to understand the
theoretical foundation as well as its practical aspects of ASP.NET Core web
application framework and C# language features.
Course Contents:
Unit 1: Language
Preliminaries (8 Hrs.)
Introduction to .Net framework,
Compilation and execution of .Net applications, Basic Languages constructs,
Constructor, Properties, Arrays and String, Indexers, Inheritance, use of
“base” keyword, Method hiding and overriding, applying polymorphism in code
extensibility, structs and enums, abstract class sealed class, interface,
Delegate and Events, Partial class, Collections, Generics, File IO, LINQ
(Language Integrated Query) Fundamentals: Lambda Expressions, Try statements
and Exceptions, Attributes: Attribute
Classes, Named and Positional
Attribute Parameters, Attribute Targets, Specifying Multiple Attributes,
Asynchronous Programming: Principle of Asynchrony, Async/Await patterns in C#
Unit 2: Introduction to
ASP.NET (3 Hrs.)
.NET and ASP.NET frameworks:
.NET, .NET Core, Mono, ASP.NET Web Forms, ASP.NET MVC, ASP.NET Web API, ASP.NET
Core, .NET Architecture and Design Principles, Compilation and Execution of
.NET applications: CLI, MSIL and CLR, .NET Core in detail, .NET CLI: build,
run, test and deploy .NET Core Applications
Unit 3: HTTP and
ASP.NET Core (3 Hrs.)
HTTP, Request and Response
Message Format, Common web application architectures, MVC Pattern, ASP.NET Core
Architecture Overview, Projects, and Conventions, ASP.NET and ASP.NET MVC
Unit 4: Creating
ASP.NET core MVC applications (10 Hrs.)
Setting up the Environment,
Controllers and Actions: Create Controllers, Create Actions and Action Results
Types, Rendering HTML with Views: Razor Syntax, Understanding Tag Helpers,
Models: Binding and Validations, URL Routing and features, Web API
Applications: API Controllers, JSON, Dependency Injection
and IOC containers
Unit 5: Working with
Database (6 Hrs.)
ADO.NET basics: Connection,
Command, Reader and Adapter classes, Entity Framework (EF) Core,
Object-Relational Mapper (ORM), Adding EF Core to an application: Choosing
database provider, data models and data context, Querying and Saving data to
database: Create, read, update and delete records
Unit 6: State
Management on ASP.NET Core Application (4 Hrs.)
State Management on stateless HTTP, Server-side strategies:
Session State, TempData, Using HttpContext, Cache Client-side strategies:
Cookies, Query Strings, Hidden Fields
Unit 7: Client-side
Development in ASP.NET Core (4 Hrs.)
Common client-side web technologies, JQuery, Forms and
Validation, Single Page Application (SPA) Frameworks: Angular, React
Unit 8: Securing in
ASP.NET Core Application (5 Hrs.)
Authentication: ASP.NET Core
Identity, Adding authentication to apps and identity service configurations,
Authorization: Roles, Claims and Policies, Securing Controllers and Action
Methods, Common Vulnerabilities: Cross-site Scripting attacks, SQL Injection
attacks, Cross-site Request Forgery (CSRF), Open Redirect Attacks
Unit 9: Hosting and
Deploying ASP.NET Core Application (2 Hrs.)
App Servers and Hosting models: IIS, Nginx, Apache, ASP.NET
Core Module, Kestrel, Docker and Containerization, Publish to Azure cloud
Laboratory works:
The laboratory work includes writing programs covering most
of the concepts of above units using C# and
.NET core SDK (3.0 or above)
Text / Reference Books:
1.
C# 8.0 and .NET Core 3.0 – Modern Cross-Platform
Development, Fourth Edition, by Mark J. Price, 2019
2.
ASP.NET Core in Action, by
Andrew Lock, 2018
3.
Learning ASP.NET Core
2.0, Michel Bruchet, Jason De Oliveira, 2017
4. Learn
ASP.NET Core 3 - Second Edition, Kenneth Yamikani Fukizi, Jason De
Oliveira, Michel Bruchet, 2019
Technical Writing
|
Technical Writing |
|
|
|
80
+ 20 |
|
CSC368 |
|
|
|
32 + 8 |
|
Theory |
|
|
|
3 |
Course Description:
This course is designed for students to enhance their skills
for workplace writing. It helps them in
the process of „listening, researching, planning, composing, revising, and
editing‟ documents for use in business, science, hi-tech, and other practical fields.
Technical Writing for Success
provides students with practical approach to producing their own proposal
content, manual instructions, informative briefs, news releases, and other
pragmatic documents. Abundant in sample documents, critical thinking questions,
and insightful writing advice on style and voice, this textbook prepares
students for successful technical writing.
Course Objectives:
Enable students to identify the importance and
characteristics of technical writing and produce some quality technical pieces
of workplace writing.
Course Detail:
Unit 1: What Is
Technical Writing (3 Hrs.)
Introduction; You Are a Technical Writer!; Characteristics
of a Technical Writing; How Technical Writing Compares to Other Writing
Unit 2: Audience and
Purpose (3 Hrs.)
Introduction; Meeting the Audience‟s Needs; Planning Your
Document‟s Purpose, Scope, and Medium
Unit 3: Writing Process
(4 Hrs.)
Introduction; A Process for Technical Writing; Planning;
Drafting and Revising; Copyediting and Publishing; Writing Collaboratively
Unit 4: Brief
Correspondence (4 Hrs.)
Introduction; Introduction to Text Messages; E-mails; Memos,
and Letters; Audience; Prewriting; Formatting; Composing the Message
Unit 5: Document Design
and Graphics (4 Hrs.)
Introduction; Designing the Document; Who Reads Graphics?;
Designing Graphics; Constructing Graphics
Unit 6: Writing for the
Web (4 Hrs.)
Introduction; Getting Started on Web Pages; Organizing and
Designing Web Pages; Writing Text for the Web; Special Web Pages
Unit 7: Information
Reports (5 Hrs.)
Introduction; Getting Started on Informative Reports;
Summary and Abstract; Mechanism and Description; Periodic Reports; Progress
Reports; News Releases
Unit 8: Employment
Communication (5 Hrs.)
Introduction; Getting Started on Employment Communication;
Formatting and Organizing Resumes; Types of Resumes; Composing Resumes;
Composing Employment Letters
Unit 9: Presentations
(5 Hrs.)
Introduction; Getting Started on Presentations; Planning;
Organizing and Composing; Preparing; Rehearsing; Presenting; Organizing a Group
Presentation
Unit 10: Recommendation
Reports (3 Hrs.)
Introduction; What Is a Recommendation Report?; Starting a
Recommendation Report; Formatting and Organizing Recommendation Reports;
Composing Recommendation Reports
Unit 11: Proposals (3
Hrs.)
Introduction; What Is a Proposal?; Getting Started on Proposal;
Composing Informal Proposals; Composing Formal Proposals
Unit 12: Ethics in the
Workplace (2 Hrs.)
Introduction; What Is Ethics?; Creating a Culture of Ethics;
What Do you When Faces with an Ethical Dilemma?; Why Is It So Difficult to
Behave Ethically?
Inside Track (Ask
students to go through the ideas discussed in this section as they make much
sense to writing. Explain if necessary.)
Text Book:
1. Smith-Worthington,
Daelene and Sue Jefferson. Technical
Writing for Success. 3rd ed. USA: Cengage Writing, 2011.
Reference Books:
1.
Anderson, Paul V. Technical
Communication: A Reader-Centered Approach. 7th ed. USA: Wadsworth Publishing, 2010.
2.
Markel, Mike and Stuart A. Selber. Technical Communication. 12th edition.
USA: Bedford Books, 2017.
3.
Tebeaux, Elizabeth and Sam Dragga. The Essentials of Technical Communication. 4th ed.
London: Oxford University Press, 2010.
Applied Logic
|
Applied Logic |
|
|
60
+ 20 + 20 |
|
CSC369 |
|
|
24 +
8 + 8 |
|
Theory + Lab |
|
|
3 |
Course Description:
This course covers different concepts of logic including
arguments, proposition and syllogism, symbolic logic, quantification,
fallacies, and reasoning.
Course Objectives:
The objectives of this course are to
•
Understand Concept of Validity and Invalidity
•
Discuss argument and fallacy analysis techniques
•
Demonstrate proof of validity and invalidity
•
Understand Syllogistic rules and immediate
inferences
•
Discuss inductive and casual reasoning
Course Contents:
Unit 1: Argument
Analysis (6 Hrs.)
1.1.
Concept of Logic, Proposition and Arguments,
Recognizing Arguments, Arguments vs Explanations, Validity and Truth, Deductive
and Inductive Arguments
1.2.
Paraphrasing Arguments, Diagramming Arguments, Complex
Argumentative Passages, Problems in Reasoning
Unit 2: Categorical
Propositions and Syllogisms (10 Hrs.)
2.1. Theory
of Deduction, Classes of Categorical Propositions, Types Categorical
Propositions, Quality, Quantity and Distribution, Square of Oppositions,
Immediate Inferences, Venn Diagrams of Categorical Propositions.
2.2. Standard
form of Categorical Syllogism, Mood and Figure, Testing Validity by Using Venn
Diagrams, Syllogistic Rules and Fallacies
2.3. Syllogistic
Arguments, Reducing Number of Terms, Translating Categorical Propositions into
Standard Form, Enthymemes and Sorites
Unit 3: Symbolic Logic
(12 Hrs.)
3.1. Modern
Logic and Symbolic Language, Conjunction, Disjunction, negation, Material
Implication, Material Equivalence
3.2. Argument
Forms and Refutation by Analogy, Testing Validity of Arguments by using Truth
Tables, Statement Forms, Logical Equivalences
3.3. Valid
Argument Forms, Formal Proof of Validity, Replacement Rules, Proof of Invalidity, Inconsistency
Unit 4: Quantification
Theory (6 Hrs.)
4.1. Need
of Quantification, Singular Propositions, Types of Quantifiers, Representing
Categorical Propositions in Quantification Theory
4.2. Generalization
and Instantiation, Proving Validity, Proving Invalidity
Unit 5: Fallacies (6 Hrs.)
5.1. Concept
and Classification of Fallacies, Fallacies of Relevance, Fallacies of Deductive
Induction, Fallacies of Presumption, Fallacies of Ambiguity
Unit 6: Analogical and
Casual Reasoning (5 Hrs.)
6.1.
Review of Induction and Deduction, Arguments by
Analogy, Analogical Arguments, Refutation by Logical Analogy
6.2.
Cause and Effect, Casual Laws, Induction by
Enumeration, Casual Analysis Methods, Limitations of Inductive Arguments
Laboratory Works:
The laboratory work includes realizing representation
techniques and makes proper inferences. Student should be able to
•
Represent complex argumentative Passages by using
Symbolic Logic
•
Generate proper reasoning and inferences to reach to
the conclusion
Recommended Books:
1.
Irving M. Copy, Carl Cohen, Priyadarshi Jetli, Monica
Prabhakar, Introduction to Logic, Pearson Publication, 14th Edition,
2013
2.
Patrick J. Hurley, A Concise introduction to Logic,
Wadsworth Publication, 12th Edition, 2014
3.
Peter Kreeft, Trent Doughherty, Socratic Logic: A Logic
Text Using Socratic Method, Platonic Question, and Aristotelian Principles, St.
Augustines Press, 3rd Edition 2010.
E-Commerce
|
E-Commerce
|
|
|
60
+ 20 + 20 |
|
CSC370 |
|
|
24 +
8 + 8 |
|
Theory + Lab |
|
|
3 |
Course Description:
This course covers the fundamental concepts of E-commerce
and E-business models, and components of E-commerce system.
Course Objectives:
The main objective of this course is to provide basic
concepts of E-commerce, E-commerce Business Models, E-Payments, E-commerce
Security, Digital Marketing, Search Engine Optimization, and Basics of
Recommendation System.
Course Contents:
Unit 1: Introduction (4
Hrs.)
E-commerce, E-business, Features of E-commerce, Pure vs.
Partial E-commerce, History of E-commerce, E-commerce Framework (People, Public
Policy, Marketing and Advertisement,
Support Services, Business Partnerships), Types of
E-commerce: B2C, B2B, C2B, C2C, MCommerce, U-commerce, Social-Ecommerce, Local
E-commerce, Challenges in Ecommerce, Status of E-commerce in Nepal, Overview of
Electronic Transaction Act of Nepal
Unit 2: E-commerce
Business Model (8 Hrs.)
E-commerce Business Model, Elements of Business Model, Types
of Revenue Models, B2C Business Models: E-tailer, Community Provider, Content
Provider, Portal, Transaction Broker, Market Creator, Service Provider, B2B
Business Models: Net Market Places (Edistributer, E-procurement, Exchanges,
Industry Consortia), Private Industrial Networks (Single Firm, Industry Wide), Electronic Data
Interchange (EDI), EDI Layered Architecture, EDI in E-commerce, E-commerce and
Industry Value Chain, Firm Value Chain, Firm Value Web, Case Studies of Global
and Local E-commerce Systems
Unit 3: Electronic
Payment System (9 Hrs.)
E-payment System, Online Credit Card Transaction, Online
Stored Value Payment System, Digital and Mobile Wallet, Smart Cards,
Social/Mobile Peer-to-Peer Payment Systems, Digital Cash/e-cash, E-Checks,
Virtual Currency, Electronic Billing Presentment and
Payment (EBPP) System,
Auctioning in E-commerce (English, Dutch, Vickery, Double),
SET Protocol, Features of SET, Participants in SET, Card
Holder Registration, Merchant Registration, Purchase Request, Dual Signature,
Payment Authorization, Payment Capture, Status of E-Payment Systems in Nepal,
Case Studies of Global and Local Payment Systems
Unit 4: Building
E-commerce System (5 Hrs.)
E-commerce Website/Software, Building Catalogs: Static,
Dynamic, Building Shopping Cart, Transaction Processing, Development of
E-commerce Website/Software: Databases, Application Programs, Integration with
ERP Systems, Integration with Payment Gateways, Using Open Source CMS for Development
of E-commerce Applications
Unit 5: Security in
E-Commerce (7 Hrs.)
E-commerce Security, Dimensions of E-commerce Security:
Confidentiality, Integrity, Availability, Authenticity, Nonrepudiation,
Privacy, Security Threats in E-commerce: Vulnerabilities in E-commerce,
Malicious Code, Adware, Spyware, Social Engineering,
Phishing, Hacking, Credit card fraud and Identity
theft, Spoofing and Pharming, Client
and Server Security, Data Transaction Security, Security Mechanisms:
Cryptography, Hash
Functions, Digital Signatures, Authentication, Access
Controls, Intrusion Detection System, Secured Socket Layer(SSL)
Unit 6: Digital
Marketing (7 Hrs.)
Digital Marketing, Online Advertisement, Ad Targeting,
Search Engine Marketing, Keyword
Advertising, Search Engine Optimization, Display Ad
Marketing, Interstitial Ad, Video Ad,
Advertising Exchanges, Programmatic Advertising, Real-Time
Bidding, E-mail Marketing, Affiliate Marketing, Social Marketing, Mobile
Marketing, Local Marketing, Online Marketing Metrics, Pricing Models for Online
Advertisements, Case Studies: Facebook
Marketing Tools, Twitter Marketing Tools, Pinterest
Marketing Tools, Location Based Marketing Tools: Google AdSense
Unit 7: Optimizing
E-commerce Systems (5 Hrs.)
Search Engine Optimization, Working mechinaism of Search
Engines, On Page SEO, Off Page SEO, Page Ranks, Using Google Aanalytics, Social
Media Analytics, Recommendation Systems: Collaborative, Content Based, Use of
Recommendation Systems in E-commerce
Laboratory Works:
The laboratory work includes developing E-commerce
applications. The students are highly encouraged to use server side and client
side scripting for developing the applications with categories, shopping carts,
payment gateways. Students can also use open source ecommerce CMS frameworks
and configure them to simulate e-commerce systems. The laboratory work for
e-comerce optimization includes SEO tools like Google Analytics, Facebook
Analytics, Twitter Analytics etc. Students can also implement basic
recommendation system in the ecommerce systems.
Text / Reference Books:
1.
Kenneth C. Laudon and Carol Guercio Traver, E-commerce
Business Technology Society, Pearson
2.
Electronic Transaction ACT of Nepal
3.
SET Secure Electronic Transaction Specification Book 1:
Business Description
4.
Efraim Turban, Jon Outland, David King, Jae Kyu Lee,
Ting-Peng Liang, Deborrah C. Turban, Electronic Commerce A Managerial and
Social Networks Perspective, Springer
5.
Gary P. Schneider, Electronic Commerce, Course
Technology, Cengage Learning
6.
Colin Combe, Introduction to E-business Management and
strategy, Elsevier
7.
Dave Chaffey, E-Business & E-Commerce Management
Strategy, Implementation And Practice, Pearson
8.
Cristian Darie and Emilian Balanescu, Beginning PHP and
MySQL E-Commerce From Novice to Professional, Apress
9.
Cristian Darie and Karli Watson, Beginning ASP.NET
E-Commerce in C# From Novice to Professional, Apress
10. Larry
Ullaman, Effortless E-commerce with PHP and MySQL, New Riders
11. Eric
Enge, Stephan Spencer, Rand Fishkin, and Jessie C. Stricchiola foreword by John
Battelle, The Art of SEO: Mastering Search Engine Optimization, O‟Reilly
12. Adam
Clarke, SEO Learn Search Engine Optimization With Smart Internet Marketing
Strategies: Learn SEO with smart internet marketing strategies
13. Charu
C. Aggrawal, Recommender Systems, Springer
Automation and
Robotics
Course Title: Automation and Robotics Full Marks: 60 + 20 + 20
Course No: CSC371 Pass Marks: 24 + 8 + 8
Nature of the Course: Theory + Lab Credit
Hrs: 3
Semester: VI
Course Description:
This course provides the detailed
idea about fields of robotics and its control mechanisms.
Course Objective:
The main objective is to provide
information on various parts of robots and idea on fields of robotics. It also
focuses on various kinematics and inverse kinematics of robots, trajectory
planning of robots and to study the control of robots for some specific
applications.
Course Contents:
Unit 1: Introduction (8 Hrs.)
Definition and Origin of
Robotics, Types of Robotics, Major Components, Historical development of Robot,
Robotic System and Robot anatomy,
Degrees of freedom, Coordinate System and its type Asimov's laws of robotics,
Dynamic stabilization of robots
Unit 2: Power Sources and Sensors (8 Hrs.)
Hydraulic, pneumatic and electric
drives, determination of HP of motor and gearing ratio, variable speed arrangements, path
determination, micro machines in robotics,
machine vision, ranging, laser, acoustic, magnetic, fiber optic and
tactile sensors.
Unit 3: Manipulators, Actuators, and
Grippers (8 Hrs.)
Manipulators, Classification,
Construction of manipulators,
manipulator dynamics and force control, electronic and pneumatic
manipulator control, End effectors, Loads and Forces, Grippers, design considerations, Robot motion
Control, Position Sensing
Unit 4: Kinematics and Path Planning (8
Hrs.)
Solution of Inverse Kinematics
Problem, Multiple Solution Jacobian Work Envelop, Hill Climbing Techniques,
Robot Programming Languages
Unit 5: Process Control (8 Hrs.)
Process Control and Types, On-Off
Control Systems, Proportional Control Systems, Proportional Plus Integral (PI)
Control Systems, Three Mode Control (PID) Control Systems, Process Control
Tuning.
Unit 6: Case Studies (5 Hrs.)
Multiple robots, Machine
Interface, Robots in Manufacturing and not-Manufacturing Application, Robot
Cell Design, Selection of a Robot
Laboratory Works:
The laboratory work should be
focused on implementation of sensors, design of control systems. It should also
deal with developing programs related Robot design and control using python.
Text Books:
1.
Mikell P. Weiss G.M., Nagel R.N., Odraj N.G., Industrial Robotics, McGraw Hill.
2.
Ghosh, Control
in Robotics and Automation: Sensor Based Integration, Allied Publishers.
References:
1.
Jain K.C. and Aggarwal B.E., Robotics – Principles and Practice, Khanna Publishers
2.
Schuler, C.A. and McNamee,
W.L. Modern Industrial Electronics, Macmillan/McGraw-Hill
3.
Klafter R.D., Chimielewski T.A., Negin M., Robotic Engineering – An Integrated
Approach, Prentice Hall of India.
4.
Deb.S.R., Robotics
Technology and Flexible Automation, John Wiley, USA 1992. 5. Asfahl
C.R., Robots and Manufacturing
Automation, John Wiley, USA 1992
6.
Mc Kerrow P.J. Introduction
to Robotics, Addison Wesley, USA, 1991.
7.
Issac Asimov I. Robot,
Ballantine Books, New York, 1986.
Neural Networks
Neural Networks
CSC372
Theory + Lab
Course Description:
The course introduces the underlying principles and design
of Neural Network. The course covers the basics concepts of Neural Network
including: its architecture, learning processes, single layer and multilayer
perceptron followed by Recurrent Neural Network
Course Objective:
The course objective is to demonstrate the concept of
supervised learning, unsupervised learning in conjunction with different
architectures of Neural Network
Course Contents:
Unit 1: Introduction to
Neural Network (4 Hrs.)
Basics of neural networks and human brain, Models of a
neuron, Neural Network viewed as Directed Graphs, Feedback, Network
Architectures, Knowledge Representation, Learning Processes, Learning Tasks
Unit 2: Rosenblatt’s
Perceptron (3 Hrs.)
Introduction, Perceptron, The Perceptron Convergence
Theorem, Relation between the Perceptron and Bayes Classifier for a Gaussian
Environment, The Batch Perceptron Algorithm
Unit 3: Model Building
through Regression (5 Hrs.)
Introduction, Linear Regression Model: Preliminary
Considerations, Maximum a Posteriori Estimation of the Parameter Vector,
Relationship Between Regularized Least-Squares Estimation and Map Estimation,
Computer Experiment: Pattern Classification, The Minimum-Description-Length
Principle, Finite Sample-Size Considerations, The instrumental- Variables
Method
Unit 4: The Least-Mean-Square Algorithm (5 Hrs.)
Introduction, Filtering Structure of the LMS Algorithm,
Unconstrained Optimization: A Review, The Wiener Filter, The Least-Mean-Square
Algorithm, Markov Model Portraying the Deviation of the LMS Algorithm from the
Wiener Filter, The Langevin Equation:
Characterization of Brownian Motion, Kushner‟s
Direct-Averaging Method, Statistical LMS Learning Theory for Small
Learning-Rate Parameter, Virtues and Limitations of the LMS Algorithm,
Learning-Rate Annealing Schedules
Unit 5: Multilayer
Perceptron (8 Hrs.)
Introduction, Batch Learning and On-Line Learning, The
Back-Propagation Algorithm, XOR problem, Heuristics for Making the
back-propagation Algorithm Perform Better, Back Propagation and
Differentiation, The Hessian and Its Role in On-Line Learning, Optimal
Annealing and Adaptive Control of the Learning Rate, Generalization,
Approximations of Functions, Cross Validation, Complexity Regularization and
Network Pruning, Virtues and
Limitations of Back-Propagation Learning, Supervised
Learning Viewed as Optimization Problem, Convolutional Networks, Nonlinear
Filtering, Small-Scale Versus Large-Scale Learning Problems
Unit 6: Kernel Methods
and Radial-Basis Function Networks (7 Hrs.)
Introduction, Cover‟s Theorem on the separability of
Patterns, The Interpolation problem, Radial-Basis-Function Networks, K-Means
Clustering, Recursive Least-Squares Estimation of the Weight Vector, Hybrid
Learning Procedure for RBF Networks, Kernel Regression and Its Relation to RBF
Networks
Unit 7: Self-Organizing Maps (6 Hrs.)
Introduction, Two Basic Feature-Mapping Models,
Self-Organizing Map, Properties of the Feature Map, Contextual Maps,
Hierarchical Vector Quantization, Kernel Self-Organizing Map, Relationship
between Kernel SOM and Kullback-Leibler Divergence
Unit 8: Dynamic Driven
Recurrent Networks (7 Hrs.)
Introduction, Recurrent Network Architectures, Universal
Approximation Theorem, Controllability and Observability, Computational Power
of Recurrent Networks, Learning Algorithms, Back Propagation through Time,
Real-Time Recurrent Learning, Vanishing Gradients in Recurrent Networks,
Supervised Training Framework for Recurrent Networks Using Non Sate Estimators,
Adaptivity Considerations, Case Study: Model Reference Applied to Neurocontrol
Laboratory works:
Practical should be focused on Single Layer Perceptron,
Multilayer Perceptron, Supervised Learning, Unsupervised Learning, Recurrent
Neural Network, Linear Prediction and Pattern Classification
Text Book:
1. Simon Haykin, Neural Networks and
Learning Machines, 3rd Edition, Pearson
Reference Books:
1.
Christopher M. Bishop, Neural Networks for Pattern
Recognition, Oxford University Press, 2003
2.
Martin T. Hagan, Neural Network Design, 2nd
Edition PWS pub co.
Computer Hardware
Design
Computer Hardware Design
CSC373
Theory + Lab
Course
Description:
This course provides the detailed idea about the design of
computer hardware.
Course Objective:
The main objective is to provide information on various
computer hardware and their design. It focuses on various concepts regarding
processor, memory and arithmetic operations. It also emphasizes on multicores,
multiprocessors and clusters. It also deals with non-functional requirements
that play vital role in the design.
Course Contents:
Unit 1: Computer
Abstractions and Technology (3 Hrs.)
Introduction, Performance, The Power Wall, The Sea Change:
The Switch from
Uniprocessors to Multiprocessors, Manufacturing and
Benchmarking the AMD Opteron X4
Unit 2: Instructions:
Language of the Computer (8 Hrs.)
Introduction, Operations of the Computer Hardware, Operands
of the Computer Hardware, Signed and Unsigned Numbers, Representing
Instructions in the Computer, Logical Operations,Instructions for Making
Decisions, Supporting Procedures in Computer Hardware, MIPS Addressing for
32-Bit Immediates and Addresses, Parallelism and Instructions, Translating and
Starting a Program, Arrays versus Pointers, Advanced Material: Compiling C and
Interpreting Java, ARM Instructions, x86 Instructions.
Unit 3: Arithmetic for
Computers (5 Hrs.)
Introduction, Addition and Subtraction, Multiplication,
Division, Floating Point, Parallelism and Computer Arithmetic: Associativity,
Real Stuff: Floating Point in the x86.
Unit 4: The Processor
(8 Hrs.)
Introduction, Logic Design Conventions, Building a Data
path, A Simple Implementation Scheme, An Overview of Pipelining, Pipelined Data
path and Control, Data Hazards: Forwarding versus Stalling, Control Hazards,
Exceptions, Parallelism and Advanced Instruction-Level Parallelism, Real Stuff:
the AMD Opteron X4 Pipeline, Advanced Topic: an Introduction to Digital
DesignUsing a Hardware Design Language to Describe andModel a Pipeline and More
Pipelining Illustrations.
Unit 5: Large and Fast:
Exploiting Memory Hierarchy (8 Hrs.)
Introduction, The Basics of Caches, Measuring and Improving
Cache Performance, Virtual
Memory, A Common Framework for Memory Hierarchies, Virtual Machines,
Using a FiniteState Machine to Control a Simple Cache, Parallelism and Memory
Hierarchies: Cache Coherence, Advanced Material: Implementing Cache
Controllers, Real Stuff: the AMD Opteron X4 and Intel Nehalem Memory
Hierarchies.
Unit 6: Storage and
Other I/O Topics (5 Hrs.)
Introduction, Dependability, Reliability, and Availability,
Disk Storage, Flash Storage, Connecting Processors, Memory, and I/O Devices,
Interfacing I/O Devices to the Processor, Memory, and Operating System, I/O
Performance Measures: Examples from Disk and File Systems, Designing an I/O
System, Parallelism and I/O: Redundant Arrays of Inexpensive Disks, Real Stuff:
Sun Fire x4 Server, Advanced Topics: Networks.
Unit 7: Multicores,
Multiprocessors, and Clusters (8 Hrs.)
Introduction, The Difficulty of Creating Parallel Processing
Programs, Shared Memory Multiprocessors, Clusters and Other Message-Passing
Multiprocessors, Hardware Multithreading, SISD, MIMD, SIMD, SPMD, and Vector,
Introduction to Graphics Processing Units, Introduction to Multiprocessor
Network Topologies, Multiprocessor Benchmarks, Roofline: A Simple Performance
Model, Real Stuff: Benchmarking Four Multicores Using theRoofline Model.
Laboratory Works:
The practical work should focus on use of hardware design
language and programming. It should also focus on x86 instructions. There
should also be practical related to processor, memory, clusters,
multithreading, Interfaces, pipelining.
Text Book:
1. David
A. Patterson and John L. Hennessy., Computer Organization and Design: The
Hardware/Software Interface, 4th Edition.
References:
1.
M. M. Mano., Computer Organization, 3rd
Edition
2.
M. M. Mano., Computer System Architecture, 3rd
Edition
Cognitive Science
Cognitive Science
CSC374
Theory + Lab
Course Description:
This course covers the fundamental concepts of cognitive
science and brain computation.
Course Objectives:
The main objective of this course is to provide basic knowledge
of web cognition process, mind theory, physical symbol systems, cognitive
systems, concepts of brain mappings and neural network structures.
Course Contents:
Unit 1: Introduction (7
Hrs.)
Cognition Process, Cognitive Psychology, Cognitive Science;
Foundations of Cognitive Science, Cognitive Science and Multi-disciplinary;
Machines and Minds; Laws thoughts to binary logic; Classical Cognitive Science;
Connectionist Cognitive Science; Mind body
Problem; Turing Response to Mind Body Problem; Pinker,
Penerose and Searle‟s Responses to Mind Body Problem; Representational Theory
of Mind; Theories of Mental Representation: Minimal Analysis of mental
representation, Resemblance theories of mental representation, Casual
covariation theories of mental representation, internal roles theories of
mental representation
Unit 2: Precursors of Cognitive Science (5 Hrs.)
Behaviorism; Theory of Computation and Algorithms;
Algorithms and Turing Machines;
Marr‟s Three Level of Computation; Linguistics and Formal
Language; Information Processing Models in Psychology
Unit 3: Psycological
Perspective of Cognition (5 Hrs)
Cognitive Models of Memory, Atkinson-Shiffrin‟s Model, Tulving‟s Model,
Mental Imagery, Kosslyn‟s View, Moyer‟s View, Peterson‟s View, Cognitive Maps,
Problem Understanding, States of Cognition, Cognition in AI
Unit 4: Physical Symbol
System and Language of Thought (7 Hrs.)
Physical Symbol System Hypothesis; Symbol and Symbol
Systems; Problem Solving by Symbol
Structure; Physical Symbol System to Language of Thoughts; The Computer Model
of the Mind; Syntax and the Language of Thought: Fodor‟s Argument for the
Language of Thought Hypothesis; The Chinese Room Argument; Chinese Room and Turing
Test; The Symbol Ground Problem
Unit 5: Cognitive
System (4 Hrs.)
Cognitive System; Architecture for intelligent agents;
Modularity of Mind; Modularity Hypothesis; The ACT-R/PM architecture
Unit 6: Brain Mapping
(6 Hrs.)
Structure and Function in Brain; Anatomical Connectivity;
Cognitive Functioning Techniques from Neuroscience; Mapping the brain‟s
electrical activity: EEG and MEG; Mapping the brain‟s blood flow and blood
oxygen levels: PET and fMRI; Attention; Visuospatial attention
Unit 7: Mind Reading (5 Hrs.)
Metarepresentation; Metarepresentation, autism, and theory
of mind; Mind Reading System; Understanding False Belief; Mind Reading as
Simulation
Unit 8: Neural Networks and Distributed Information
Processing (6 Hrs.)
Neurally Inspired Models of Information Processing;
Single-Layer Networks and Boolean Functions; Multilayer Networks; Information
Processing in Neural Networks; Language Learning in Neural Networks; Neural
Network Models of Children‟s Physical Reasoning
Laboratory Works:
The laboratory work includes implementing and simulating the
concepts of cognition process, intelligent agents, neural networks. In
addition, laboratory work can be extended to use the tools like PSY Toolkit, PsyNeuLink etc.
Text Book / Reference
Books:
1.
José Luis
Bermúdez, Cognitive Science: An Introduction to the Science of the Mind,
Cambridge University Press
2.
Michael R. W. Dawson , Mind, Body, World: Foundations
of Cognitive Science, UBC Press
3.
Daniel Kolak, William Hirstein, Peter Mandik, Jonathan
Waskan, Cognitive Science, An Introduction to Mind and Brain, Routledge Taylor
and Francis Group
4.
Amit Konar – Artificial Intelligence and Soft
computing: Behavioral and Cognitive Modeling of the Human Brain, CRC Press
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