This document outlines a course on advanced programming paradigms. It covers five units: introduction to programming paradigms and languages; Java programming paradigms including object-oriented, procedural, and declarative; advanced Java including concurrent and graphical interfaces; Python paradigms including functional, logic, and parallel; and formal paradigms including automata and symbolic programming with Python. The course aims to develop understanding of paradigm functionalities and deploy various paradigms including structural, object-oriented, declarative, and graphical interfaces using Java and Python applications. Learning is assessed through unit tests, projects, reports, and final examinations.
The following resources come from the 2009/10 B.Sc in Media Technology and Digital Broadcast (course number 2ELE0076) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
There is an increasing demand for embedding intelligence in software systems as part of its core set of features both in the front-end (e.g. conversational user interfaces) and back-end (e.g. prediction services). This combination is usually referred to as AI-enhanced software or, simply, smart software.
The development of smart software poses new engineering challenges, as now we need to deal with the engineering of the “traditional” components, the engineering of the “AI” ones but also of the interaction between both types that need to co-exist and collaborate.
In this talk we'll see how modeling can help tame the complexity of engineering smart software by enabling software engineers specify and generate smart software systems starting from higher-level and platform-independent modeling primitives.
But, unavoidably, these models will be more diverse and complex than our usual ones. Don't despair, we'll also see how some of these same AI techniques that are making our modeling life challenging can be turned into allies and be transformed into modeling assistants to tackle the engineering of smart software with a new breed of smart modeling tools.
The following resources come from the 2009/10 B.Sc in Media Technology and Digital Broadcast (course number 2ELE0076) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
There is an increasing demand for embedding intelligence in software systems as part of its core set of features both in the front-end (e.g. conversational user interfaces) and back-end (e.g. prediction services). This combination is usually referred to as AI-enhanced software or, simply, smart software.
The development of smart software poses new engineering challenges, as now we need to deal with the engineering of the “traditional” components, the engineering of the “AI” ones but also of the interaction between both types that need to co-exist and collaborate.
In this talk we'll see how modeling can help tame the complexity of engineering smart software by enabling software engineers specify and generate smart software systems starting from higher-level and platform-independent modeling primitives.
But, unavoidably, these models will be more diverse and complex than our usual ones. Don't despair, we'll also see how some of these same AI techniques that are making our modeling life challenging can be turned into allies and be transformed into modeling assistants to tackle the engineering of smart software with a new breed of smart modeling tools.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
mlflow: Accelerating the End-to-End ML lifecycleDatabricks
Building and deploying a machine learning model can be difficult to do once. Enabling other data scientists (or yourself, one month later) to reproduce your pipeline, to compare the results of different versions, to track what’s running where, and to redeploy and rollback updated models is much harder.
In this talk, I’ll introduce MLflow, a new open source project from Databricks that simplifies the machine learning lifecycle. MLflow provides APIs for tracking experiment runs between multiple users within a reproducible environment, and for managing the deployment of models to production. MLflow is designed to be an open, modular platform, in the sense that you can use it with any existing ML library and development process. MLflow was launched in June 2018 and has already seen significant community contributions, with over 50 contributors and new features including language APIs, integrations with popular ML libraries, and storage backends. I’ll show how MLflow works and explain how to get started with MLflow.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
mlflow: Accelerating the End-to-End ML lifecycleDatabricks
Building and deploying a machine learning model can be difficult to do once. Enabling other data scientists (or yourself, one month later) to reproduce your pipeline, to compare the results of different versions, to track what’s running where, and to redeploy and rollback updated models is much harder.
In this talk, I’ll introduce MLflow, a new open source project from Databricks that simplifies the machine learning lifecycle. MLflow provides APIs for tracking experiment runs between multiple users within a reproducible environment, and for managing the deployment of models to production. MLflow is designed to be an open, modular platform, in the sense that you can use it with any existing ML library and development process. MLflow was launched in June 2018 and has already seen significant community contributions, with over 50 contributors and new features including language APIs, integrations with popular ML libraries, and storage backends. I’ll show how MLflow works and explain how to get started with MLflow.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
MASS MEDIA STUDIES-835-CLASS XI Resource Material.pdf
syllabus app.pdf
1. Unit 1 – INTRODUCTION TO PROGRAMMING PARADIGM
Programming Languages – Elements of Programming languages - Programming Language Theory - Bohm- Jacopini structured program theorem - Multiple Programming Paradigm – Programming Paradigm hierarchy – Imperative Paradigm: Procedural, Object-
Oriented and Parallel processing – Declarative programming paradigm: Logic, Functional and Database processing - Machine Codes – Procedural and Object-Oriented Programming – Suitability of Multiple paradigms in the programming language - Subroutine,
method call overhead and Dynamic memory allocation for message and object storage - Dynamically dispatched message calls and direct procedure call overheads – Object Serialization – parallel Computing.
Unit 2 – JAVA PROGRAMMING PARADIGMS
Object and Classes; Constructor; Data types; Variables; Modifier and Operators – Structural Programming Paradigm: Branching, Iteration,Decision making, and Arrays – Procedural Programming Paradigm: Characteristics; Function Definition; Function Declaration
and Calling; Function Arguments – Object-Oriented Programming Paradigm: Abstraction; Encapsulation; Inheritance; Polymorphism; Overriding - Interfaces: Declaring, Implementing; Extended and Tagging - Package: Package Creation.
Unit 3 – ADVANCED JAVA PROGRAMMING PARADIGMS
Concurrent Programming Paradigm: Multithreading and Multitasking; Thread classes and methods – Declarative Programming Paradigm:Java Database Connectivity (JDBC); Connectivity with MySQL – Query Execution; - Graphical User Interface Based
Programming Paradigm: Java Applet: Basics and Java Swing: Model View Controller (MVC) and Widgets; Develop a java project dissertation based on the programming paradigm.
Unit 4 – PYTHONIC PROGRAMMING PARADIGM
Functional Programming Paradigm: Concepts; Pure Function and Built-in Higher-Order Functions; Logic Programming Paradigm: Structures, Logic, and Control; Parallel Programming Paradigm: Shared and Distributed memory; Multi-Processing – Ipython; Network
Programming Paradigm: Socket; Socket Types;Creation and Configuration of Sockets in TCP – Client / Server Model.
Unit 5 – FORMAL AND SYMBOLIC PROGRAMMING PARADIGM
Automata Based programming Paradigm: Finite Automata – DFA and NFA; Implementing using Automaton Library - Symbolic Programming Paradigm: Algebraic manipulations and calculus; Sympy Library - Event Programming Paradigm: Event Handler; Trigger
functions and Events – Tkinter Library. Develop a python-based project dissertation based on the programming paradigm.
Course
Code
21CSC203P Course
Name
Advanced Programming Practice Course
Category
P Professional Core L T P C
3 1 0 4
Pre-requisite
Courses
Nil
Co-requisite
Courses
Nil
Progressive
Courses
Nil
Course Offering Department Computational Intelligence Data Book / Codes/Standards
Course Learning Rationale (CLR): The purpose of learning this course is to: Program Outcomes (PO)
CLR-1 Understand the paradigm functionalities and their hierarchy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CLR-2 Deploy structural, procedural, and Object-Oriented Programming Paradigm
Engineering
Knowledge
Problem
Analysis
Design
&
Development
Analysis,
Design,
Research
Modern
Tool
Usage
Society
&
Culture
Environment
&
Sustainability
Ethics
Individual
&Team
Work
Communication
Project
Mgt.
&
Finance
Life
Long
Learning
PSO
-
1
PSO
–
2
PSO
–
3
CLR-3 Demonstrate the event, Graphical User Interface, and declarative Paradigm with a java application.
CLR-4 Extended knowledge on logic, functional, network and concurrent Paradigm
CLR-5 Symbolic, Automata-based, and Event with a python application.
Course Outcomes (CO): (CO): At the end of this course, learners will be able to:
CO-1 Devise solutions to the various programming paradigm 3 2 - - - - - - - - - - - - -
CO-2 Express proficiency in the usage of structural, procedural, and Object-Oriented Program 3 2 - 1 - - - - - - - - - - -
CO-3 Determine the Java application using declarative, event, and graphical user interface paradigm 3 - 2 1 2 - - - 1 - - - - - -
CO-4 Express proficiency in the usage of logic, functional, network, and concurrent Paradigm 3 2 - 1 - - - - - - - - - - -
CO-5 Determine the Python application using symbolic, automata-based, and graphical user interface programming paradigms 3 - 2 1 2 - - - 1 - - - - - -
Learning
Resources
1. Elad Shalom, A Review of Programming Paradigms throughout the
History: With a suggestion Toward a Future Approach, Kindle Edition,
2018
2. Maurizio Gabbrielli , Simone Martini, Programming Languages:
Principles and Paradigms, 2010.
3. Herbert Schildt, Java: The Complete Reference Seventh Edition,
2016.
4. Mark Lutz, Programming Python: Powerful Object-Oriented
Programming, 2011.
Learning Assessment
Bloom’sLevel of Thinking
Continuous Learning Assessment (CLA) - By the CourseFaculty By The CoE
CLA-1 Average of Unit
test(20%)
CLA-2 Project Based
Learning (60%)
Report and Viva Voce
(20% Weightage)
Final Examination
(0% weightage)
Theory Practice Theory Practice Theory Practice Theory Practice
Level 1 Remember 30 - -- 20 - 10 - -
Level 2 Understand 30 - - 20 - 10 - -
Level 3 Apply 20 - - 20 - 10 - -
Level 4 Analyze 20 - - 20 - 10 - -
Level 5 Evaluate - - - 10 - 30 - -
Level 6 Create - - - 10 - 30 - -
Total 100 % 100 % 100 % -