Cis 520 hw solution

Your project has been staffed and you are about to meet with the team for the first time. Create a slide show in PowerPoint or similar software in which you address the following, in this order:. Goals: What the project hopes to accomplish. Critical Success Factors: Identify at least 4 different stakeholders; for each, list at least 2 things that the stakeholder requires in order to deem the project successful.

Acquisition strategy: Should the system be built in-house, created by a contractor, purchased offthe-shelf and customized, or leased as a service? Explain your rationale.

Otherwise, identify 3 candidate organizations that can deliver the system. System functions: In a table format, summarize the types of users for the system; the business reason s each would use the system; the ways that the system supports each of these needs; and how this support differs from the current system.

Connectivity: Provide a diagram that shows how the system will connect to the other information systems and what data flows among them. Security: List the most serious cybersecurity threats and vulnerabilities of the new system.

Suggest strategies to address them. Include a title and summary slide. Use one slide for each of the 8 points above. Include speaker notes or audio narration that explains each slide more fully. Factors: Identify at least 4 different stakeholders; for each, list at least 2 things that stakeholder requires in order to deem the project successful.

Explained why each stakeholder would be most interested in 2 things to deem project successful. Acquisition strategy: Should the system be built in-house, created by a contractor, purchased off-the-shelf and customized, or leased as a service? Explain the rationale for your choice. Analyzed various acquisition strategies, or presented an acquisition strategy that was not previously mentioned. Limited recommendation for development lifecycle for inhouse development, or three external organizations that can deliver the system.

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Determined resources needed for the acquisition strategy, but did not justify reasoning. Recommended a project development lifecycle for inhouse development, or three external organizations that can deliver the system, but did not justify why they were chosen. Recommended a project development lifecycle for inhouse development, or three external organizations that can deliver the system.

Proposed a combination of resources and strategy to organize stakeholder efforts toward project completion. System functions: In a table format, summarize the types of users for the system; the business. Connectivity: Provide a diagram that shows the other information systems this one will connect to, and what data flows among them.

cis 520 hw solution

Showed other information systems that your system would connect to, but hard to follow. Explained how diagram would be updated and maintained based on changes in the marketplace.

cis 520 hw solution

Inadequate speaker notes or audio narration, too much or too little information on each slide provided. Clarity, persuasion, proper communication, writing mechanics, and formatting requirements. Somewhat clear structure, limited persuasion, grammatical errors, language too simple or too wordy.This course provides a thorough modern introduction to the field of machine learning.

CIS/500 CIS500 CIS 500 WEEK 10 ASSIGNMENT 2

It is designed for students who want to understand not only what machine learning algorithms do and how they can be used, but also the fundamental principles behind how and why they work. See the Course Description and Lectures for more details.

Prerequisites are a basic knowledge of linear algebra matrices, eigenvectorsprobability, statistics, programming in python,and latex. See the Course Description.

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We will try our best to accommodate as many qualified students as possible, but we typically receive more registration requests than course capacity. Lecture recordings are on Canvasbut note that lectures that use the board are very hard to follow from the recordings and that recordings sometimes fail. Attendance is strongly encouraged. The Scheduleincluding exam dates, is in Lectures. Assignments will be posted in Canvas. Your homeworks will be submitted via Gradescope. For questions on course material and assignments, use Piazza.

Home Page. Welcome to CIS Machine Learning This course provides a thorough modern introduction to the field of machine learning.Create a package named hw3.

The first two values represent the top-left coordinates of the red brick in the first row. The next two values are the width and height of each brick. The last value represents the space between each brick along any direction. Declare another integer instance variable numberOfRows representing the number of rows desired for the pattern.

In the constructor specify the appropriate window title using your lastName and assign the instance variables in step a with the values 50 50 30 and 2 respectively.

HSA/520 HSA520 HSA 520 WEEK 5 MIDTERM EXAM

In the constructor initialize the numberOfRows with the value 5. In the paint method declare two local variables named currentX and currentY. Assign the startX and startY variables to these local variables respectively.

Using nested loops draw the pattern for the desired numberOfRows. Note that the pattern should work for other values as well. There should be only a single fillRect method in the code. The fillRect method in the code should only make use of the variables currentX currentY brickWidth brickHeight and brickSpace.

In the main method create the application object set its size to by and its visibility to true to test the above graphics. Question Details Normal. Question posted by. Available Solution. Submitted On 09 May, Solution posted by. Solution of the above question Buy now to view full solution. Other Related Solutions. Handshake payment:. Solution price:. Payable price:. Confirm Cancel. Join our newsletter. Be the first to receive exciting news, features and special offers from coursemerit. Please enter an email address.

CourseMerit is not sponsored or endorsed by any college or university.See the course FAQ for various hints on the homework. Grades are posted by the last three digits of your Penn ID number. Remember to use turnin to submit executible versions of your homework. This schedule may be modified. Required Readings. Optional Readings. Introduction pdf Probability review.

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Elements 1, 2 linear algebra review mostly for much later in the class or alternate or a short web page. MTWR matlab help. Introduction to Matlab Moore A. Matlab Info.

Elements 3. Instance-based learning pdf. HW0 due solutiongrading scheme - now available. AI Logistic regression pdf and supplement pdf.

cis 520 hw solution

Logistic Regression. Elements 4. Overfitting and cross validation pdf. HW1 due. The data glass. HW2 due solutiongrading scheme. Feature selection as search, stepwise regression pdf. Neural Networks pdf. Elements HW3 due solutioncodeand grading scheme. Infomation Theory pdf. HW4 due solutioncode and grading scheme.

Elements 9. Decision Trees pdf.

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Elements 7. Huffman codingInformation Theory. Belief Nets - Representation pdf ; AI Belief Nets - Inference pdf.To browse Academia. Skip to main content. Log In Sign Up. CIS Homework 1. Roscoe Casita. The original DFA had 10 states that tran- sitioned from 9 to 0. The problem was that 17 was now a valid input. This resolved the problem. There are only two paths that return to the final accepted state.

In the diagram the 3 independent locks can be seen easily, along with the shortcut routes to for overlaps. Then we define the Cartesian product demonstrating the intersection and the union of the two DFAs.

Then we substitute each letter w1 with x1 in the language A where appropriate.

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We are simply replacing on a string by string basis the replacement of transitions themselves. The actual content and meaning of the strings has no bearing or effect, and simply changing the literal contents of the string with another, does not change the behavior of the machine itself. First we describe the definitions for clarities sake: 1.

As soon as this condition is satisfied, we need to apply the entire concatenation of the new set again, in order to satisfy the new condition Thus all sets that are transitive must have some kind of closure applied to them. Related Papers.

Bachelor's Thesis: Learning of DFAs and subsequential transducers using membership, translation and strong equivalence queries. By Alexander Aprelkin. On the state complexity of partial word DFAs. Partial Word DFAs.On this page… hide. We will update this page periodically with extra explanations for problems that we are getting a lot of questions about. Take a look at the ML Math page that we just put up — that has some pointers to brief review notes that should hopefully be a lot more helpful than trying to plow through a whole textbook.

Also, for a quick review of common probability distributions, check out the appendix in the Pattern Recognition textbook. Note that each time you turn in, the previous submission is deleted. If you use Windows, you need to install an SSH client, for example as described here:. Then you can connect to eniac. Then, once you have your file on your SEAS account, you can use turnin on the command line following the instructions we gave before. You should be able to solve this using just the expectation rules and formulas given in the question.

If you are integrating or taking derivatives for this problem, you are working harder than you have to.

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For example, if you thought the posterior was a Gaussian distribution, you would say so and then state its mean and variance, since these are the two parameters associated with a Gaussian distribution. When you write out the formula for this expectation, you will see how to use the hint. Try switching the order of summations if you are having trouble seeing how to use marginalization.

For each k, find and plot the mean of the train means so, this is the average of an average and the standard deviation of these train means.

Do the same for the test values. You will end up with 4 train points with standard deviations draw a line through the points, use err bars to plot the deviations and 4 test points with standard deviations draw a line through the points, use err bars to plot the deviations. Note that you may be able to better vectorize your code than in the pseudocode shown above explicitly using for-loops in matlab is slower than doing matrix and vector operationsbut this pseudocode does outline the correct overall procedure.

Homework 1. On this page… hide General I need more probability background. Can you point me to some textbooks? How do I use turnin?The goal of Machine Learning is to build computer systems that can adapt and learn from their experience. In recent years we have seen a surge of applications that make use of machine learning technologies and one can argue that Machine learning has been essential to the success of many recent technologies, from natural language technologies Siri, search technology, automated advertising, text correction to computer vision technologies image recognition applications, autonomous vehiclesgenomics, medical diagnosis, social network analysis, and many others.

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This course will introduce some of the key machine learning methods that have proven valuable and successful in practical applications. We will discuss some of the foundational questions in machine learning -- when and why does learning work -- in order to get a good understanding of the basic issues in this area, and present the main paradigms and techniques needed to obtain successful performance in application areas such as natural language and text understanding, speech recognition, computer vision, data mining, adaptive computer systems and others.

These include methods for learning linear representations, decision-tree methods, Bayesian methods, kernel based methods and neural networks methods, as well as clustering, dimensionality reduction and reinforcement learning methods. We will also discuss how to model problems as machine learning problems, how to evaluate learning algorithms, and how to deal with some real-world issues such as noisy data, and domain adaptation.

Students registered for the graduate version of this course CIS will be required to complete additional work throughout the semester. This work will include a course project, and possibly additional components to the homework and the exams. Since the two versions have different requirements, you cannot complete the course as CIS and later petition to have it changed to CIS for graduate credit; if you're considering changing this course to CIS for graduate credit, you should register for the graduate version now.

This section briefly describes the differences between these courses. The course is cross-listed between undergraduate and graduate versions; the graduate course has somewhat different requirements as described below.

The plan is for students will leave this class with a good understanding of the key issues in Machine Learning, and with a solid background of how to model and apply machine learning to their problems. And, you should take CIS if you see yourself doing research in Machine Learning, research that requires developing new ML methods, and you are confident in your mathematical background.

Otherwise, you can access it here and email it to ddeutsch seas. Homework 2 is now released. The deadline has been extended to Oct. The assignment for Project Proposals has been created on Gradescope. Note that there is no need to submit on Canvas this time. The Final Project Presentation will be held during the last class, and the Reports will be due on December 20, Course Description The goal of Machine Learning is to build computer systems that can adapt and learn from their experience.


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