- The document is a programming tutorial that introduces Python and MATLAB for programming in medical imaging.
- It discusses what a computer program is, explains programming languages and code, and why learning to program is useful.
- The tutorial compares Python and MATLAB, noting that both can be used for the course but examples will focus on MATLAB. It outlines differences like MATLAB requiring a license while Python is free.
This document provides an introduction to a course on programming in C++. The course will cover fundamental programming concepts like variables, data types, functions, and arrays. It will also cover more advanced C++ topics like pointers, references, and dynamic memory allocation. The goal is for students to learn essential programming skills and gain familiarity with the C++ syntax so they can write reasonably complex programs. The course will be assessed through quizzes, midterm and final exams, and practice projects completed in labs.
This document provides an introduction and overview for a course on programming in C++. It discusses the goals of the course, which are to teach programming principles and the C++ language. Students will learn essential concepts like variables, data types, functions, and arrays. They will write increasingly complex programs and develop good programming style. The course will be assessed through quizzes, exams, and class projects. Topics to be covered include variables, input/output, control flow, arrays, pointers, strings, and file I/O. Good programming practices like readability, simplicity, and avoiding reinventing solutions are emphasized.
This document provides an overview of a computer programming course that teaches C++. The course aims to introduce programming concepts in C++ and object orientation. It will include weekly lectures and labs. Students will need to complete lab exercises to practice the weekly concepts. Assessment will include assignments, quizzes, a project, midterm exam, and final exam. Recommended resources like books and websites on C++ are also provided.
Here are the solutions to the printing practice:
print("My Name")
print("Blue", "Green", "Red")
print(26 * 66 + 13)
print(6804 / 162)
print(2 ** 8)
print("66+33")
66+33
So putting a mathematical expression in double quotes prints the expression as a string, rather than evaluating it.
This document contains a lesson on introduction to Python programming and variables. It includes 6 tasks to introduce key concepts like using the Python shell to perform calculations, creating and assigning variables of different data types (integer, float, string), inputting values into variables, joining and printing variable values, doing calculations with user-input variables, and creating a program to calculate employee pay using variables. The objectives are to understand how to do simple calculations in Python, use variables, understand different data types, and join/print variable statements.
This document discusses ways to raise the bar of software development by combining it with other disciplines. It proposes 6 intersections between software design and other fields: 1) Software Design and UX, 2) Other design disciplines, 3) Materials Science, 4) Math, 5) Engineering principles of built-in self-testing, and 6) Reaching out to scientists. It argues that considering ideas from other domains could improve software design and help address challenges like dependencies in legacy code. The document also suggests ways to fundamentally change development by precisely defining requirements and generating code from them.
Here are the programs for the assignments:
1.
name = "John"
print(name)
2.
x = 5
y = 10
z = 15
print(x, y, z)
3.
mood = "happy"
strength = 80.5
rank = 1
10 more lessons learned from building Machine Learning systems - MLConfXavier Amatriain
Ìý
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. It is important to focus on feature engineering to create features that are reusable, transformable, interpretable, and reliable. The outputs of models may become inputs to other models, so care must be taken to avoid feedback loops and ensure proper data dependencies.
Xavier Amatriain, VP of Engineering, Quora at MLconf SF - 11/13/15MLconf
Ìý
10 More Lessons Learned from Building Real-Life ML Systems: A year ago I presented a collection of 10 lessons in MLConf. These goal of the presentation was to highlight some of the practical issues that ML practitioners encounter in the field, many of which are not included in traditional textbooks and courses. The original 10 lessons included some related to issues such as feature complexity, sampling, regularization, distributing/parallelizing algorithms, or how to think about offline vs. online computation.
Since that presentation and associated material was published, I have been asked to complement it with more/newer material. In this talk I will present 10 new lessons that not only build upon the original ones, but also relate to my recent experiences at Quora. I will talk about the importance of metrics, training data, and debuggability of ML systems. I will also describe how to combine supervised and non-supervised approaches or the role of ensembles in practical ML systems.
10 more lessons learned from building Machine Learning systemsXavier Amatriain
Ìý
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. The outputs of machine learning models will often become inputs to other models, so models need to be designed with this in mind to avoid issues like feedback loops.
This document discusses computer tools for academic research. It aims to make computer use more effective for research tasks like downloading data, running regressions, and writing papers. The course covers programming principles, version control, data management beyond spreadsheets, modular Python programming, testing code, and numeric computing tools. It uses a sample research project on social networks and app adoption to illustrate these tools. The document compares the academic research cycle to software development and argues that following good programming practices can help optimize researchers' time.
Start machine learning in 5 simple stepsRenjith M P
Ìý
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
Workshop slides which give an overview of python programming. The slides are accompanied by DIY (do it yourself) programs which can be found as in GitHub (https://github.com/bhalajin/blueprints)
More information, visit: http://www.godatadriven.com/accelerator.html
Data scientists aren’t a nice-to-have anymore, they are a must-have. Businesses of all sizes are scooping up this new breed of engineering professional. But how do you find the right one for your business?
The Data Science Accelerator Program is a one year program, delivered in Amsterdam by world-class industry practitioners. It provides your aspiring data scientists with intensive on- and off-site instruction, access to an extensive network of speakers and mentors and coaching.
The Data Science Accelerator Program helps you assess and radically develop the skills of your data science staff or recruits.
Our goal is to deliver you excellent data scientists that help you become a data driven enterprise.
The right tools
We teach your organisation the proven data science tools.
The right hands
We are trusted by many industry leading partners.
The right experience
We've done big data and data science at many clients, we know what the real world is like.
The right experts
We have a world class selection of lecturers that you will be working with.
Vincent D. Warmerdam
Jonathan Samoocha
Ivo Everts
Rogier van der Geer
Ron van Weverwijk
Giovanni Lanzani
The right curriculum
We meet twice a month. Once for a lecture, once for a hackathon.
Lectures
The RStudio stack.
The art of simulation.
The iPython stack.
Linear modelling.
Operations research.
Nonlinear modelling.
Clustering & ensemble methods.
Natural language processing.
Time series.
Visualisation.
Scaling to big data.
Advanced topics.
Hackathons
Scrape and mine the internet.
Solving multiarmed bandit problems.
Webdev with flask and pandas as a backend.
Build an automation script for linear models.
Build a heuristic tsp solver.
Code review your automation for nonlinear models.
Build a method that outperforms random forests.
Build a markov chain to generate song lyrics.
Predict an optimal portfolio for the stock market.
Create an interactive d3 app with backend.
Start up a spark cluster with large s3 data.
You pick!
Interested?
Ping us here. signal@godatadriven.com
Cis 1403 lab1- the process of programmingHamad Odhabi
Ìý
This lab aims to develop students knowledge and skills needed to create a simple programming code. It covers the process of developing computer programs starting from a simple analysis of the problem, identifying outputs, inputs, and design process/algorithm, convert algorithm to code, testing, and documentation. The student will be introduced to the Java program structure, numerical variable and high-level introduction to data types. The lab does not go into depth explaining the data types and memory storage. These will be discussed in the upcoming labs. Also, the student will be introduced to the REPL cloud environment that will be used to create a simple application.
This document provides a teacher's guide for teaching Practical C++ Programming. It includes notes, suggestions, and review questions for each chapter to help instructors teach the key concepts and demonstrate programs. The guide aims to make teaching C++ easier by providing extra context and guidance for classroom presentation. It covers topics ranging from basic program structure and style to more advanced concepts like classes, pointers, files, and templates.
This document provides an introduction to computer programming skills for civil engineering students. It discusses why engineers need programming abilities and how computer modeling is important for engineering problems. The lecture covers algorithms, finite element analysis, and examples of programming applications in areas like fluid dynamics, geotechnics, and groundwater flow. Limitations of models and the importance of verification and validation are also addressed. An example algorithm for extracting annual maximum river discharge data from daily records is presented to illustrate programming concepts.
(Prog213) (introduction to programming)v1Aaron Angeles
Ìý
The document discusses object-oriented programming and problem solving. It covers:
1. Programming involves developing instructions to carry out tasks using objects and their interactions.
2. The programming process involves problem analysis, designing a general solution, and implementing and testing the program.
3. Object-oriented programming models real-world problems as objects that interact. Problem solving techniques like identifying objects are discussed.
This guide provides a refresher on basic computer programming concepts without using a specific programming language. It defines key terms like variables, which represent values that can change throughout a program, and statements, which are the smallest standalone elements a computer can understand. It also explains functions and methods as named sets of instructions that can be reused, and parameters as values passed into functions. Finally, it outlines different data types like integers, doubles, strings, and booleans that variables can take on to store different kinds of values.
Approaches to teaching primary computingJEcomputing
Ìý
The document discusses pedagogical approaches for teaching primary computing. It provides objectives around the primary computing curriculum and computational thinking concepts. It then describes several unplugged activities that can be used to develop computational thinking without computers, such as writing algorithms for making sandwiches or drawing characters. Finally, it discusses strategies for teaching computing, including developing independence, paired programming, debugging, differentiation, and assessment.
nothing at all for programming site<ggggggAnasAshraf34
Ìý
This document summarizes a physical session that included various team-building and programming activities:
1. The session started with an icebreaker where participants shared facts about themselves and had to guess which one was false.
2. They then learned tips for problem-solving and completed a challenge to program a simple calculator app using block-based coding.
3. In the second half of the session, participants worked on tasks involving text-based coding in RoboMind to complete maps and navigate a robot through mazes, applying programming concepts.
Faster Python Programs Through Optimization by Dr.-Ing Mike MullerPyData
Ìý
1) The document discusses several methods for optimizing Python programs to increase speed, including profiling CPU usage with the cProfile module.
2) cProfile can measure the time spent in different functions and identify bottlenecks by running sample programs and printing statistics.
3) Running a "fast" sample that calls a function taking 0.001 seconds 100 times took 0.195 seconds total, with most time spent in the time.sleep calls. A "slow" sample taking 0.1 seconds per call was over 10 seconds total.
1. The document summarizes a keynote presentation on whether Python is still production ready.
2. It discusses that Python has evolved significantly since its creation in 1990 to better support modern programming patterns like asynchronous programming.
3. It argues that Python can be considered "production ready" because it has a large community and ecosystem that helps reduce maintenance costs, libraries are well documented and tested, and the language continues to be improved while maintaining backwards compatibility.
This document provides an overview of various concepts in software engineering, including implementation, testing, debugging, development rules, and sayings around software development. It discusses principles like debugging and maintenance taking more time than implementation, data structures being more important than codes/algorithms, avoiding premature optimization, and releasing software often for early feedback. It also covers topics such as unit testing, avoiding obese code, intellectual property, management approaches, software development methodologies, and different types of testing.
Python is the choice llanguage for data analysis,
The aim of this slide is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of the steps you need to learn to use Python for data analysis.
This document provides an introduction and overview of MATLAB. MATLAB is a high-performance language for technical computing, computation, visualization, and programming. It is useful for tasks like math and computation, algorithm development, modeling, simulation, data analysis, scientific and engineering graphics, and application development. MATLAB represents everything as matrices and is an interpreted language, so no compilation is needed. It has a vast library of functions and is widely used in teaching and research. While it is good for rapid prototyping, MATLAB may be slow for some processes and not well-suited for large-scale system development or web applications. The document provides examples of MATLAB code and discusses how images can be represented and manipulated as matrices in MATLAB.
The document discusses optimal control and linear quadratic control (LQR). It introduces LQR as a method for optimal control where the system dynamics are linear and the cost is quadratic. It explains that LQR provides an algorithm to optimize the controller by finding the feedback gain matrix K. The document provides steps to solve LQR problems using MATLAB and finds the optimal regulator to minimize a cost function for a sample linear time-invariant system.
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Here are the programs for the assignments:
1.
name = "John"
print(name)
2.
x = 5
y = 10
z = 15
print(x, y, z)
3.
mood = "happy"
strength = 80.5
rank = 1
10 more lessons learned from building Machine Learning systems - MLConfXavier Amatriain
Ìý
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. It is important to focus on feature engineering to create features that are reusable, transformable, interpretable, and reliable. The outputs of models may become inputs to other models, so care must be taken to avoid feedback loops and ensure proper data dependencies.
Xavier Amatriain, VP of Engineering, Quora at MLconf SF - 11/13/15MLconf
Ìý
10 More Lessons Learned from Building Real-Life ML Systems: A year ago I presented a collection of 10 lessons in MLConf. These goal of the presentation was to highlight some of the practical issues that ML practitioners encounter in the field, many of which are not included in traditional textbooks and courses. The original 10 lessons included some related to issues such as feature complexity, sampling, regularization, distributing/parallelizing algorithms, or how to think about offline vs. online computation.
Since that presentation and associated material was published, I have been asked to complement it with more/newer material. In this talk I will present 10 new lessons that not only build upon the original ones, but also relate to my recent experiences at Quora. I will talk about the importance of metrics, training data, and debuggability of ML systems. I will also describe how to combine supervised and non-supervised approaches or the role of ensembles in practical ML systems.
10 more lessons learned from building Machine Learning systemsXavier Amatriain
Ìý
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. The outputs of machine learning models will often become inputs to other models, so models need to be designed with this in mind to avoid issues like feedback loops.
This document discusses computer tools for academic research. It aims to make computer use more effective for research tasks like downloading data, running regressions, and writing papers. The course covers programming principles, version control, data management beyond spreadsheets, modular Python programming, testing code, and numeric computing tools. It uses a sample research project on social networks and app adoption to illustrate these tools. The document compares the academic research cycle to software development and argues that following good programming practices can help optimize researchers' time.
Start machine learning in 5 simple stepsRenjith M P
Ìý
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
Workshop slides which give an overview of python programming. The slides are accompanied by DIY (do it yourself) programs which can be found as in GitHub (https://github.com/bhalajin/blueprints)
More information, visit: http://www.godatadriven.com/accelerator.html
Data scientists aren’t a nice-to-have anymore, they are a must-have. Businesses of all sizes are scooping up this new breed of engineering professional. But how do you find the right one for your business?
The Data Science Accelerator Program is a one year program, delivered in Amsterdam by world-class industry practitioners. It provides your aspiring data scientists with intensive on- and off-site instruction, access to an extensive network of speakers and mentors and coaching.
The Data Science Accelerator Program helps you assess and radically develop the skills of your data science staff or recruits.
Our goal is to deliver you excellent data scientists that help you become a data driven enterprise.
The right tools
We teach your organisation the proven data science tools.
The right hands
We are trusted by many industry leading partners.
The right experience
We've done big data and data science at many clients, we know what the real world is like.
The right experts
We have a world class selection of lecturers that you will be working with.
Vincent D. Warmerdam
Jonathan Samoocha
Ivo Everts
Rogier van der Geer
Ron van Weverwijk
Giovanni Lanzani
The right curriculum
We meet twice a month. Once for a lecture, once for a hackathon.
Lectures
The RStudio stack.
The art of simulation.
The iPython stack.
Linear modelling.
Operations research.
Nonlinear modelling.
Clustering & ensemble methods.
Natural language processing.
Time series.
Visualisation.
Scaling to big data.
Advanced topics.
Hackathons
Scrape and mine the internet.
Solving multiarmed bandit problems.
Webdev with flask and pandas as a backend.
Build an automation script for linear models.
Build a heuristic tsp solver.
Code review your automation for nonlinear models.
Build a method that outperforms random forests.
Build a markov chain to generate song lyrics.
Predict an optimal portfolio for the stock market.
Create an interactive d3 app with backend.
Start up a spark cluster with large s3 data.
You pick!
Interested?
Ping us here. signal@godatadriven.com
Cis 1403 lab1- the process of programmingHamad Odhabi
Ìý
This lab aims to develop students knowledge and skills needed to create a simple programming code. It covers the process of developing computer programs starting from a simple analysis of the problem, identifying outputs, inputs, and design process/algorithm, convert algorithm to code, testing, and documentation. The student will be introduced to the Java program structure, numerical variable and high-level introduction to data types. The lab does not go into depth explaining the data types and memory storage. These will be discussed in the upcoming labs. Also, the student will be introduced to the REPL cloud environment that will be used to create a simple application.
This document provides a teacher's guide for teaching Practical C++ Programming. It includes notes, suggestions, and review questions for each chapter to help instructors teach the key concepts and demonstrate programs. The guide aims to make teaching C++ easier by providing extra context and guidance for classroom presentation. It covers topics ranging from basic program structure and style to more advanced concepts like classes, pointers, files, and templates.
This document provides an introduction to computer programming skills for civil engineering students. It discusses why engineers need programming abilities and how computer modeling is important for engineering problems. The lecture covers algorithms, finite element analysis, and examples of programming applications in areas like fluid dynamics, geotechnics, and groundwater flow. Limitations of models and the importance of verification and validation are also addressed. An example algorithm for extracting annual maximum river discharge data from daily records is presented to illustrate programming concepts.
(Prog213) (introduction to programming)v1Aaron Angeles
Ìý
The document discusses object-oriented programming and problem solving. It covers:
1. Programming involves developing instructions to carry out tasks using objects and their interactions.
2. The programming process involves problem analysis, designing a general solution, and implementing and testing the program.
3. Object-oriented programming models real-world problems as objects that interact. Problem solving techniques like identifying objects are discussed.
This guide provides a refresher on basic computer programming concepts without using a specific programming language. It defines key terms like variables, which represent values that can change throughout a program, and statements, which are the smallest standalone elements a computer can understand. It also explains functions and methods as named sets of instructions that can be reused, and parameters as values passed into functions. Finally, it outlines different data types like integers, doubles, strings, and booleans that variables can take on to store different kinds of values.
Approaches to teaching primary computingJEcomputing
Ìý
The document discusses pedagogical approaches for teaching primary computing. It provides objectives around the primary computing curriculum and computational thinking concepts. It then describes several unplugged activities that can be used to develop computational thinking without computers, such as writing algorithms for making sandwiches or drawing characters. Finally, it discusses strategies for teaching computing, including developing independence, paired programming, debugging, differentiation, and assessment.
nothing at all for programming site<ggggggAnasAshraf34
Ìý
This document summarizes a physical session that included various team-building and programming activities:
1. The session started with an icebreaker where participants shared facts about themselves and had to guess which one was false.
2. They then learned tips for problem-solving and completed a challenge to program a simple calculator app using block-based coding.
3. In the second half of the session, participants worked on tasks involving text-based coding in RoboMind to complete maps and navigate a robot through mazes, applying programming concepts.
Faster Python Programs Through Optimization by Dr.-Ing Mike MullerPyData
Ìý
1) The document discusses several methods for optimizing Python programs to increase speed, including profiling CPU usage with the cProfile module.
2) cProfile can measure the time spent in different functions and identify bottlenecks by running sample programs and printing statistics.
3) Running a "fast" sample that calls a function taking 0.001 seconds 100 times took 0.195 seconds total, with most time spent in the time.sleep calls. A "slow" sample taking 0.1 seconds per call was over 10 seconds total.
1. The document summarizes a keynote presentation on whether Python is still production ready.
2. It discusses that Python has evolved significantly since its creation in 1990 to better support modern programming patterns like asynchronous programming.
3. It argues that Python can be considered "production ready" because it has a large community and ecosystem that helps reduce maintenance costs, libraries are well documented and tested, and the language continues to be improved while maintaining backwards compatibility.
This document provides an overview of various concepts in software engineering, including implementation, testing, debugging, development rules, and sayings around software development. It discusses principles like debugging and maintenance taking more time than implementation, data structures being more important than codes/algorithms, avoiding premature optimization, and releasing software often for early feedback. It also covers topics such as unit testing, avoiding obese code, intellectual property, management approaches, software development methodologies, and different types of testing.
Python is the choice llanguage for data analysis,
The aim of this slide is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of the steps you need to learn to use Python for data analysis.
This document provides an introduction and overview of MATLAB. MATLAB is a high-performance language for technical computing, computation, visualization, and programming. It is useful for tasks like math and computation, algorithm development, modeling, simulation, data analysis, scientific and engineering graphics, and application development. MATLAB represents everything as matrices and is an interpreted language, so no compilation is needed. It has a vast library of functions and is widely used in teaching and research. While it is good for rapid prototyping, MATLAB may be slow for some processes and not well-suited for large-scale system development or web applications. The document provides examples of MATLAB code and discusses how images can be represented and manipulated as matrices in MATLAB.
The document discusses optimal control and linear quadratic control (LQR). It introduces LQR as a method for optimal control where the system dynamics are linear and the cost is quadratic. It explains that LQR provides an algorithm to optimize the controller by finding the feedback gain matrix K. The document provides steps to solve LQR problems using MATLAB and finds the optimal regulator to minimize a cost function for a sample linear time-invariant system.
This document discusses scripts and functions in MATLAB. It begins by explaining that scripts are sets of commands saved with a .m file extension that run sequentially when the file is called. Functions are similar but declare inputs and outputs, and run in their own workspace. The document provides an example of a simple function that calculates trigonometric functions of an input angle. It also discusses function help text, workspaces, debugging techniques like breakpoints, and best practices for writing efficient code.
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This document provides an overview of various topics related to control system modeling and analysis using MATLAB. It begins with introducing different ways to build models for linear time-invariant systems including continuous and discrete time transfer functions and state-space models. It then discusses how to combine models using block diagram manipulation functions in MATLAB. The document covers analyzing the transient response using step, impulse and ramp inputs. It also introduces frequency response analysis using Bode plots and stability analysis using Nyquist plots. The overall summary is that the document provides an introduction to modeling and analyzing control systems in MATLAB focusing on transfer functions, state-space models, transient response and frequency domain analysis.
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2) A model following controller that matches the closed-loop response from the command signal to the output to a specified model.
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1) The document describes a fuzzy-PID cascade controller used to control the level of liquid in a horizontal tank.
2) The cascade controller uses a PID controller as the inner loop to regulate flow rate and a fuzzy logic controller as the outer loop to control the liquid level.
3) Experimental results show that the cascade controller has a faster response time, lower steady state error, and is less affected by disturbances than a simple controller.
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Prelims of Kaun TALHA : a Travel, Architecture, Lifestyle, Heritage and Activism quiz, organized by Conquiztadors, the Quiz society of Sri Venkateswara College under their annual quizzing fest El Dorado 2025.
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APM event hosted by the South Wales and West of England Network (SWWE Network)
Speaker: Aalok Sonawala
The SWWE Regional Network were very pleased to welcome Aalok Sonawala, Head of PMO, National Programmes, Rider Levett Bucknall on 26 February, to BAWA for our first face to face event of 2025. Aalok is a member of APM’s Thames Valley Regional Network and also speaks to members of APM’s PMO Interest Network, which aims to facilitate collaboration and learning, offer unbiased advice and guidance.
Tonight, Aalok planned to discuss the importance of a PMO within project-based organisations, the different types of PMO and their key elements, PMO governance and centres of excellence.
PMO’s within an organisation can be centralised, hub and spoke with a central PMO with satellite PMOs globally, or embedded within projects. The appropriate structure will be determined by the specific business needs of the organisation. The PMO sits above PM delivery and the supply chain delivery teams.
For further information about the event please click here.
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In Odoo 17, the Inventory module allows us to set up reordering rules to ensure that our stock levels are maintained, preventing stockouts. Let's explore how this feature works.
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QuickBooks Desktop to QuickBooks Online How to Make the MoveTechSoup
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If you use QuickBooks Desktop and are stressing about moving to QuickBooks Online, in this webinar, get your questions answered and learn tips and tricks to make the process easier for you.
Key Questions:
* When is the best time to make the shift to QuickBooks Online?
* Will my current version of QuickBooks Desktop stop working?
* I have a really old version of QuickBooks. What should I do?
* I run my payroll in QuickBooks Desktop now. How is that affected?
*Does it bring over all my historical data? Are there things that don't come over?
* What are the main differences between QuickBooks Desktop and QuickBooks Online?
* And more
Computer Application in Business (commerce)Sudar Sudar
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The main objectives
1. To introduce the concept of computer and its various parts. 2. To explain the concept of data base management system and Management information system.
3. To provide insight about networking and basics of internet
Recall various terms of computer and its part
Understand the meaning of software, operating system, programming language and its features
Comparing Data Vs Information and its management system Understanding about various concepts of management information system
Explain about networking and elements based on internet
1. Recall the various concepts relating to computer and its various parts
2 Understand the meaning of software’s, operating system etc
3 Understanding the meaning and utility of database management system
4 Evaluate the various aspects of management information system
5 Generating more ideas regarding the use of internet for business purpose
Mate, a short story by Kate Grenville.pptxLiny Jenifer
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A powerpoint presentation on the short story Mate by Kate Greenville. This presentation provides information on Kate Greenville, a character list, plot summary and critical analysis of the short story.
How to Manage Putaway Rule in Odoo 17 InventoryCeline George
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Inventory management is a critical aspect of any business involved in manufacturing or selling products.
Odoo 17 offers a robust inventory management system that can handle complex operations and optimize warehouse efficiency.
Computer Network Unit IV - Lecture Notes - Network LayerMurugan146644
Ìý
Title:
Lecture Notes - Unit IV - The Network Layer
Description:
Welcome to the comprehensive guide on Computer Network concepts, tailored for final year B.Sc. Computer Science students affiliated with Alagappa University. This document covers fundamental principles and advanced topics in Computer Network. PDF content is prepared from the text book Computer Network by Andrew S. Tenanbaum
Key Topics Covered:
Main Topic : The Network Layer
Sub-Topic : Network Layer Design Issues (Store and forward packet switching , service provided to the transport layer, implementation of connection less service, implementation of connection oriented service, Comparision of virtual circuit and datagram subnet), Routing algorithms (Shortest path routing, Flooding , Distance Vector routing algorithm, Link state routing algorithm , hierarchical routing algorithm, broadcast routing, multicast routing algorithm)
Other Link :
1.Introduction to computer network - /slideshow/lecture-notes-introduction-to-computer-network/274183454
2. Physical Layer - /slideshow/lecture-notes-unit-ii-the-physical-layer/274747125
3. Data Link Layer Part 1 : /slideshow/lecture-notes-unit-iii-the-datalink-layer/275288798
Target Audience:
Final year B.Sc. Computer Science students at Alagappa University seeking a solid foundation in Computer Network principles for academic.
About the Author:
Dr. S. Murugan is Associate Professor at Alagappa Government Arts College, Karaikudi. With 23 years of teaching experience in the field of Computer Science, Dr. S. Murugan has a passion for simplifying complex concepts in Computer Network
Disclaimer:
This document is intended for educational purposes only. The content presented here reflects the author’s understanding in the field of Computer Network
Computer Network Unit IV - Lecture Notes - Network LayerMurugan146644
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programming_tutorial_course_ lesson_1.pptx
1. Programming tutorial course
Lesson 1: Why learn to program?
Python vs Matlab;
The Basics: variables, scripts and
functions
Dr Michael Berks
michael.berks@manchester.ac.uk
2. Why do I
need to
learn ‘it’?
What is
Matlab?
Because the
course leader said
so…
What is
python?
3. What is
Matlab/python?
What does computer
programming mean?
What is a
programming
language?
What is code?
What is a computer
program?
4. What is a computer
program?
Something you use to achieve some task
on a computer (or phone, tablet etc)…
Word
Write a letter, essay, lab report etc…
Excel
Analyse results, produce graphs for your report
etc…
Angry Birds
Kill time when you’re sitting in boring programming
lectures…
5. What is a computer
program?
In medical imaging…
Image(s)
Lab results
Patient data
DATA
Do something clever…
PROGRAM
Hopefully
clinically useful!
RESULT
6. What is a computer
program?
In medical imaging…
DATA
Measure volume of
grey/white matter…
PROGRAM
Predict whether
the patient has
Alzheimer’s
Disease
RESULT
MR images of the brain
7. What is a computer
program?
In medical imaging…
DATA
Measure amount dense
breast tissue…
PROGRAM
Predict risk of
developing
breast cancer
RESULT
Mammogram acquired
during routine screening
+ patient data
8. What is a computer
program?
In medical imaging…
DATA
Measure change in size
of tumour…
PROGRAM
Tell doctor/drugs
company if the
drug is working
RESULT
MRI liver tumours before
and after drug treatment
9. What does computer
programming mean?
Writing your own computer programs...
… telling the computer to do exactly what
you want.
What is a
programming
language?
A way of translating human logic into
commands a computer understands…
… like human languages, there a lots of
different languages (often similar to each
other), each with a specific set of rules
(syntax, grammar) to obey.
What is ‘code’?
A chunk of commands in a specific
programming language…
A program consists of bits of code put
together in a logical way …
… by using other people’s code, you can incorporate
bits of their program in your own (as long as you’re
using the same language!).
10. Why do I need to learn
how to write my own
computer code?
Think of it like learning to
cook…..
Now the big question….
11. You’re hungry and want
something good to
eat…..
Get parents to cook
Go to a restaurant
Get a microwave meal
x x
x
12. Vs
Heating someone else’s
food in the microwave
Ok if they’ve cooked
exactly what you want
Cooking your own food
Can eat exactly as
you like it
Vs
Using a computer
program
Ok if it does exactly what
you want
Writing your own
computer program
Make it do exactly
what you want
Research is likely to be here
14. How good a
cook/programmer do
I need to be?
Do I need to write all
my programs from
scratch?
No! Just like in cooking, you don’t need
make everything from raw ingredients, can
use pre-made pasta, sauces, wine etc…
Remember you can use other people’s code to
include bits of their programs in yours …
… but you do need to know the basics to put those bits
together (how to chop an onion, when to add seasoning etc.)
Vs
15. And finally….
What is Matlab?
Matlab is both a program and a programming language ...
… it has lots of tools you can just use to analyse data and
images. But you can also write code to extend it to do any
analysis you like (although unlike a ‘pure’ language, it will only
work if the Matlab program is installed on the computer).
Excel Photoshop
Writing
your own
code
Using other
people’s
code
+ + +
= A really powerful tool for imaging research!
17. How does python differ from Matlab?
• It’s free
– No need for an expensive license*
– Widely used, both in academia and industry
– Often installed as part of OS
• No single development environment
– This can make it a little trickier get started
– But, also more flexible
– We’ll use Spyder on this course
*Currently free because of COVID, but usually you have to pay for Matlab!
18. Course aims
• To show you how to use Matlab and/or python
• To introduce some basic programming ideas
common to all programming languages
• To give examples of some tasks in medical imaging
processing
• To provide code you can reuse later in your projects
• To make some pretty pictures!
19. I’m a bit confused…
Should I use Matlab…
..or Python?
20. For this course, consider Matlab and
python as interchangeable…
you can use either or both.
I will demonstrate mainly in Matlab,
but everything we do will be very
similar in both… just watch out for the
subtle differences!
21. vs
If you see this I’m referring to
Matlab only
If you see this I’m referring to
differences in python
22. First tasks
• Open Matlab (if you have it installed)
• Open Spyder (if you have it installed)
• Open a windows explorer window
– Go to the MSc folder you created for Professor’s Cootes,
maths course
– Make a new folder Programming
– Inside Programming, make new folders:
• Matlab
• Python
• Data
• TutorialºÝºÝߣs
23. First tasks
• Open an internet explorer/Google chrome window
• Go to the following webpage:
– http://personalpages.manchester.ac.uk/staff/michael.berks
– Click on the Matlab Tutorial tab
– Bookmark this page for future weeks
• Save the week 1 powerpoint slides and tutorial exercise into
the TutorialºÝºÝߣs folder
– These will also be added to the ‘Course content’ section of blackboard
• Leave the window open (we’ll need it later)
• Open the ‘lesson 1’ powerpoint file you’ve just saved
– As I’m going through the slides, follow them on your computer
24. Lesson Overview
• Using the Matlab/Spyder interface
• Doing maths in the Command Window
• Assigning variables and suppressing visual output
• Collecting sequences of commands: scripts
• An introduction to functions
• Basic programming tips: expressive variable names
and comments
26. Command window
• A fancy scientific calculator
– 2+3
– 2*3
– 2/3
– 2^3
• Our first python difference! It’s 2**3 in python
– 2^0.5
– 2+3*6/2
– (2+3)*6/2
• Try using ↑ and ↓
– Select earlier expressions and edit them
Any of the text in navy blue is code
you can run in the command
window. Try copy and pasting, then
hitting return
28. Assigning Variables
• Use ‘=‘ to assign variables
– Keeps data in memory to use again
– a = 4
– b = 3 + 6
– y = (4*a^2 +3*b^3) / 5
• Can also self-assign:
– b = b*2
• Check the work space
31. Suppressing visual output
• Try a = rand(100)
• This creates a 100 x 100 matrix of random
numbers
• Use clc to clear the Command Window
• Try a = rand(100);
32. Let's compute the number of
seconds in a year…
• Let's create some variables
– seconds_in_minute = 60
– minutes_in_hour = 60
– hours_in_day = 24
– days_in_year = 365
• Then compute:
– seconds_in_year = seconds_in_minute*
minutes_in_hour*hours_in_day*days_in_year
33. Scripts
• Use clear to wipe the current memory
– Check that the workspace is now empty
• Click the ‘New script’ button (top left of main menu)
– Opens a blank page in the editor
• Copy the previous commands to compute the number of
seconds in a year from the Command History
• Paste them into the blank document
• Save the document as
– Matlab: programming_tutorial_lesson1_script.m
– Python: programming_tutorial_lesson1_script.py
34. Calling a script from the command-line
• Type: programming_tutorial_lesson1_script at
the command line
• Note the lack of ‘.m’
• Check your work-space
• We'll look at different ways of calling python
scripts in future lessons
35. How many seconds in…
• 2 years?
• 10 years?
• N-years?
• Scripts are useful… but if we want to re-run
our script with different inputs, we should use
a function
36. Matlab Functions
• In our script, type %%
• This creates a new cell – a part of a script that can be run separately
• Now at the BOTTOM of the script …
– function [seconds_in_n_years] = compute_seconds_in_years(n_years)
– seconds_in_minute = 60
– minutes_in_hour = 60
– hours_in_day = 24
– days_in_year = 365
– seconds_in_year = seconds_in_hour *hours_in_day*days_in_year
– seconds_in_n_years = n_years * seconds_in_year
– end
• Type another %% above the function, then
– compute_seconds_in_years(2)
37. Python Functions
• Again type %%
• Now type …
– def compute_seconds_in_years(n_years)
seconds_in_minute = 60
minutes_in_hour = 60
hours_in_day = 24
days_in_year = 365
seconds_in_year = seconds_in_hour *hours_in_day*days_in_year
seconds_in_n_years = n_years * seconds_in_year
return seconds_in_n_years
• Run this cell, no output is generated but the function is now
available to use…
• Type another %%, and then:
– compute_seconds_in_years(10)
39. Inputs and output
• The variable n_years is an input to our function
• You will often see this described as an argument
• The variable seconds_in_n_years is the output of our
function
• We look next at using multiple inputs
• We can also have multiple outputs but will see those
in future lessons
40. Exercises
• Lookup ‘Newton's law of universal gravitation’ (yes, you can
use wikipedia)
• In Matlab, use the command prompt to compute the force
of attraction between the moon and the earth
– You can assume
• Earth’s mass: 5.97x1024 kg
• Moon’s mass: 7.35x1022 kg
• Distance from earth to moon: 380,000km
• Gravitational constant: 6.674×10−11 N m2 kg−2
• Rewrite this as a function that takes as input, M1, M2 and r
• Use commas to separate multiple inputs to your function
• Can we write this in python?
41. Variable, script and function names
• Must start with a letter
• Followed by any number of letters, digits, or
underscores.
• Matlab and python are case sensitive
– A and a, my_fun and My_fun, etc are not the same name
• Certain keywords cannot be used
– if, for, end, etc
• Be expressive: try and use names that
– Describe what functions do
– Describe what variables are
42. Scripts summary
• Scripts allow us to run sequences of commands
• All data is stored in the main workspace, as if we
typed the commands in the command window
• We can run parts of scripts by
– Selecting text and hitting F9
– Using %% to create ‘cells’
Even when ‘hacking around’ use scripts, date tagged
(e.g. work2013_02_11) to run commands
– That way you have a record of your work
– Think of them as your Matlab/Python lab book
43. Functions summary
• Functions group commands so that we can run them
with different inputs
• We have looked at defining and calling functions
inside scripts
• Next week we will look at how we can call functions
from other places (hint: you've already done this!)
• Matlab and python have 1,000s of in-built functions,
by learning how to call those we can create really
powerful programs