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This document provides guidelines for the final project report format for the COSC 4P76 Machine Learning course. The report should be concise and provide all important information to evaluate the project, while not exceeding 11 single-spaced pages. It should include sections on introducing the problem and approach, formally defining the problem, describing the learning system and algorithm, providing experimental results and analysis, and concluding remarks. The guidelines provide details on what should be included in each section.
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quotes from the reading as part of your answer, using quotation marks and the
in-text citation (Leopold, 1949/2010, p. ____).
3. Thinking back over the course, what would you embrace as your preferred
ethical theory? To answer, fill in the blanks in the following sentence: “An
action is right if it _____, and wrong if it ____.” You could endorse one of the
theories we have covered, create your own personal theory, or maybe
combine several theories in the way you see best. In addition to stating your
theory, give some reasons why it is a reasonable approach to ethics.
Summary Questions – Sartre, Leopold
1
IE 468 / OR 442
Term Project
Report due date: By midnight before the presentation day
Presentation day: TBA
Conduct a simulation study on one of the following systems:
1) SIUE cafeteria in MUC
With frequent complaints from students about waiting times in lines during their short
lunch (or dinner) breaks, you are asked to find the most economic and effective ways to cut
average total time in system by 15%. To this end, your team must develop simulation
models, evaluate various configurations/scenarios, and propose the best one. To improve
performance of the system, you could modify the layout or the process, add more resource
units, or provide any suggestions to achieve the goal (you may not be able to achieve 15%
though, which is fine).
2) Any other system of your interest
In this case, you must submit a brief project proposal by 10/24 and get the instructor’s
approval.
Guidelines
1. Data collection & curve fitting: Visit the site during the busiest hours of operation (lunch
or dinner time) if possible. You must collect data about interarrival times to the cafeteria,
and service times in at least one vendor. For other processes, make reasonable
assumptions on their distributions based on some observations and/or interviews. You
may also have to make some assumptions in some parts of your simulation model that
are hard to quantify/model/program (e.g. limited data due to limited resource
availability, customers’ mental behavior, etc.). Please make the assumptions as realistic
as possible and provided some justification about your choice. For facilitating data
collection, there are some stopwatch apps available on App Store or Google Play such as
“StopWatch Xtreme Free” that are quite handy for recording.
reportDescription.docxETME 4143LThermodynamics and Heat Tran.docx
reportDescription.docx
ETME 4143L
Thermodynamics and Heat Transfer Laboratory
Formal Report
Assignment Description
The purpose of the formal report is to comprehensively communicate results of your experiment using your pre-lab report as a draft. Formal reports must include the following sections.
The instructor will conduct a preliminary review of each report to evaluate technical writing skills. Upon the discretion of the instructor, reports that do not significantly satisfy the criteria outlined in the Technical Writing Evaluation Checklist will earn a grade of 50 with no further consideration.
Cover Page (5 points)
The formal report requires a specific cover page which is available on Moodle.
Table of Contents (5 points)
The Table of Contents lists all section and sub-section titles and the page numbers that correspond to the beginning of every section and sub-section. Its format should conform to that specified in the Chicago Manual of Style.
Overall Report Quality (10 points)
A technical report should be written with an emphasis placed on format, presentation of tables and figures, and overall writing skills. See document titled Technical Writing Evaluation Checklist for more detail.
Summary (15 points)
The Summary is limited to 300 words and does not include nor reference tables, graphs, or figures that may be included in the body of your report. Its purpose is to communicate key messages excerpted from other sections of your report, which is why it is written last. It must explain why the experiment was conducted, its scientific and/or practical relevance, results of calculations, and a brief discussion and conclusion about the results.This section must stand alone.You must assume that it is the only part of the report that will actually be read by your supervisor or client.
The Summary section must briefly answer each of the following questions:
· What was the purpose of the experiment?
· What is its scientific and/or practical relevance?
· How was the experimentconducted? What equipment was used?
· What engineering assumptions were made when analyzing sampledata?
· What are the key results based on experimentaldata? Include specific values with units of measure.
· Do results fall within expected ranges? Justify based on theory or research.
· What are some possible limitations or sources of error associated with the experiment and/or data analysis?
· What conclusions or recommendations should be made based on experimental resultsand why?
The order of these questions may change depending on how you structure this section of the report. Be sure to change to past tense verbs and report actual experimental data and results prior to cutting and pasting from your pre-lab report.
Introduction (10 points)
This section provides a comprehensive description of the engineering theory, practical applications, and contemporary issues associated with the experiment. It first briefly reiterates the purpose of the experiment and ...
Scenario 4
The systems analyst will analyze the existing systems at Dar Es Salaam High School and recommend a new system to administer staff and student records. The analyst will design the new system based on examining current documentation, distributing questionnaires to users, and interviewing users. Once developed, the analyst will produce user and technical documentation for the new system, including test plans to evaluate the system's performance.
Data Analysis – Technical learnings
The document provides resources and recommendations for learning various technical skills relevant to data analysis and machine learning, including linear algebra, calculus, statistics, probability, SQL, Python, and Excel. It lists popular online courses, books, and tutorials for learning each topic, ranging from beginner to advanced levels. It emphasizes the importance of mathematics, statistics, and SQL for data professionals and recommends spending 2-3 weeks mastering basic descriptive and inferential statistics through hands-on problems.
Technical research writing
To get PhDs, Masters and Bachelors??
To provide solutions to complex problems
To investigate laws of nature
To make new discoveries
To develop new products
To save costs
To improve our life
Human desires
LearningOutcomesassessedin
Lea
rning
Outcomes
assessed
in
this
assignment
Assignment
Title
Development of a Sorting Program.
Weighting
6
0
%
Word
Count
or
Equivalent
5
00
words
plus
implementation
and
demonstration.
1. Apply appropriate processes (design documentation and testing) and articulate the rationale for design decisions throughout the process of developing a program.
2. Communicate own algorithmic solutions for complex programming problems using appropriate notation.
3. Select and implement appropriate structures for file handling and error catching within a program.
4. Select and implement appropriate structures for data manipulation within a program.
5. Demonstrate knowledge of subject terminology and concepts, giving appropriate comparativeexamples in specific contexts.
Task(s)
Design, implement and demonstrate a program to sort a dataset from a file in to ascending order using three different algorithms. The program will time stamp the different algorithms to test the effectiveness (timeliness) of the algorithms. The methods you should user are Insertion, Selection and Bubble sort.
The program should read from a file in to an appropriate data structure. The program should then use a series of methods as discussed to sort the data and write it to another file in a sorted ascending format you have chosen. The program will calculate the length of time it took each algorithm to process the sorts and this information will be stored in another test file, which will not be overwritten only appended.
Extend the program to allow the user to select different files for sorting. Add a method to accept an additional algorithm of your own design. Your algorithm will sort the dataset in to descending order again; you will have to calculate run time. The program should continue to run until the user terminates the program.
Design documentation (500 words plus designs):
All design documentation should be in a formal report format, with a clear introduction. Any research undertaken or resources consulted should be discussed and/or presented in a bibliography. The designs should be presented in the appendices and clearly referred to from the body of the text. The following should be included:
· Identify any assumptions you have had to make (this is NOT information given in the brief)
· Discuss any research or resources you have used throughout the development, with appropriate citations.
· Given the brief evaluate the program clearly identify any areas of the brief not achieved or issues yet to be resolved
· A bibliography (all resources consulted)
· Formal design documentation in the form of use cases, Pseudo code and Flow-charts where applicable to the scenario, (in the appendices). This should be as complete as possible and reflect the final design. They main contain a number of elements that are not implemented.
Implementation:
The implemented program should demonstrate an appropriate use of programm ...
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...
The work is about using Simulated Annealing Algorithm for the effort estimation model parameter
optimization which can lead to the reduction in the difference in actual and estimated effort used in model
development.
The model has been tested using OOP’s dataset, obtained from NASA for research purpose.The data set
based model equation parameters have been found that consists of two independent variables, viz. Lines of
Code (LOC) along with one more attribute as a dependent variable related to software development effort
(DE). The results have been compared with the earlier work done by the author on Artificial Neural
Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and it has been observed that the
developed SA based model is more capable to provide better estimation of software development effort than
ANN and ANFIS
Photo+Elastic+Stress+Laboratory+Experiment
This document provides instructions for remotely conducting a photoelastic stress analysis experiment. Students will use a remote desktop link to access stress analysis equipment including a tensile testing machine and photoelastic materials. Through communication with a lab technician, students will calibrate the camera, capture images of a component under increasing loads, and analyze the stress data. Students are asked to compile an experimental report discussing stress distributions and the effect of varying loads, following guidelines provided in the appendices on report structure, initial calibration, and stress analysis methods.
Automated Essay Grading using Features Selection
This document describes research on developing an automated essay grading system using feature selection. The researchers extracted various features from essays, including bag-of-words, word and sentence counts, and structure. They used a sequential forward feature selection algorithm to identify the optimal subset of features that achieved the highest grading accuracy. This algorithm starts with an empty feature set and sequentially adds individual features, retaining features that improve the accuracy at each step. The goal is to develop a system that can grade essays consistently and reduce the labor required from human graders.
Week 6 Creating Bibliographies and Practicing MLA Citation
Week 6: Creating Bibliographies and Practicing MLA Citation
Revision Narrative Assignment Sheet and Rubric
After feedback has been received from your peer group and course instructor, you are
responsible for making appropriate changes and reflecting upon these changes from initial draft to
final submitted draft. This assignment is reflective in nature and you are not expected to quote
assigned reading or course content. Instead, think about the assignment holistically and trace
your drafting stages from beginning to final product.
Our personal identities and experiences affect how we view ourselves as writers, and we need a
certain level of self-awareness as writers in order to recognize the power and control we have to
guide how we compose texts. This Revision Narrative Worksheet should help you approach two
outcomes: demonstrating motivated decision-making and revision throughout an ongoing process
of composition and articulating connections between one’s identity and experiences and the
rhetorical choices one makes. As you answer the questions on this worksheet, narrate how your
awareness and decisions changed across the revision process for this project. Notes:
• Be as specific as possible about the realizations and decisions you narrate, but don’t feel
like you have to include every little thing you realized or did.
• The “work on your own” category is where you can describe steps you took to revise
beyond the required steps I ask you to complete. It could include additional exercises you
did on your own outside of class, visits to academic resource centers, things you applied
based on any readings, feedback you solicited from someone outside of class, etc. Either
replace the “work on your own” box with a description of what you did or delete those rows
if you’re not using them.
• To successfully complete this Revision Narrative worksheet, your answers should do the
following:
o demonstrate the use of a variety of revision strategies
o articulate the potential influence or consequences of one’s identity and experiences
as the composer
o reflect on the affective experiences of the composition process
You do not need to integrate all of the suggested changes, but goal of this assignment is to get
you thinking about the writing process at large and consider the steps you take when you revise
your own composition. To receive full credit, review the assignment rubric below and complete the
following revision narrative outline.
Criteria
Below Expectation
(0-9)
Meeting Expectations
(10-17)
Outstanding
(18-25)
Total
Points
Nuts and Bolts
Did student fully complete the revision narrative template meeting a minimum total of
500 words? Did students submit in an acceptable word .doc or .docx format? Did
student demonstrate strong close reading strategies and meaningfully integrate the
suggestions provided from peer and instructor feedback? If feedback was not
integr ...
June 2011 scenario 4
The systems analyst will analyze the current systems at Dar Es Salaam High School and design a new ICT system. As part of the analysis, the analyst will research the current systems through methods like questionnaires, interviews, and examining documents. The analyst will then design the new system and produce documentation for both users and technical staff. This will include systems documentation outlining the overall design and program documentation for individual software components. Test results from evaluating the new system will be recorded in a table and used to refine any issues found before full implementation.
fdocuments.in_unit-2-ooad.ppt
This document provides an overview of object-oriented methodologies, including Rumbaugh's Object Modeling Technique (OMT), Booch methodology, Jacobson's methodologies, patterns, frameworks, the Unified Modeling Language (UML), and common UML diagrams. It describes the key phases and models of OMT, Booch, and Jacobson's approaches. It also defines what patterns and frameworks are and compares them. Finally, it explains the history and concepts of UML and provides brief descriptions of common UML diagrams like class, use case, activity, sequence, and collaboration diagrams.
Scenario 4
Dar Es Salaam High School has recently formed by merging several smaller schools. The head teacher wants to implement a new ICT system to manage staff and student records. A systems analyst has been hired to analyze the existing systems and recommend an improved one. The new system must produce many reports quickly and allow individual records to be found rapidly. The analyst will design the new system based on evaluating the current systems, then produce documentation for users and technical details.
Formal meth
Formal methods in software engineering allow specifications to be created using mathematical notations like set theory and logic, making them less ambiguous than informal specifications. This is illustrated with an example of a block handler system in an operating system, where its state, data invariants, and operations are formally defined. While formal methods reduce errors, they have high startup costs and training needs, so are best for safety-critical systems where failures carry high risks.
part 1 - intorduction data structure 2021 mte.ppt
The document outlines a course on data structures and algorithms that is divided into 6 main parts covering various data structures, algorithms for sorting and searching, and programming concepts. The objectives of the course are to familiarize students with data structures, algorithms, and analyzing algorithm efficiency. Assessment will be based on assignments, projects, exams, and class participation.
Exam – june 2010 – qp 11
The systems analyst will research the current systems at Dar Es Salaam High School through questionnaires, interviews, and examining documents. The analyst will then design a new integrated ICT system, producing documentation on the system design and technical details. After development, the system will be evaluated using test results recorded in a table comparing expected and actual results. This will help evaluate the system and identify any needed refinements.
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Report
- 1. COSC 4P76 Machine Learning: Project Report Format This article contains the guidelines on the expected final report of your project. As much as possible, try to adhere to these general guidelines. Please note that the report should be concise and to the point but it should provide all the important information needed to evaluate your project. It looks long, but this is because it is very detailed. I recommend that you follow these guidelines. Nonetheless, there is some flexibility, and you can adjust some of the sections that are irrelevant in your case, to fit to your specific project. If there are sections you would like to add, you are welcome to do so as long as you remain within the general framework. Your report for the final project should NOT exceed 11 single-spaced pages (including references, plus or minus two pages is acceptable) using 11pt font. NO HANDWRITTEN report will be marked. Use the IEEE article template. (see http://www.ieee.org/portal/pages/pubs/transactions/stylesheets.html) 1. Introduction Briefly specify the problem you are working on and give a brief background of the problem. It is recommended that you discuss in short traditional ways of solving it and related work, if you are aware of such. Give the motivation to work on this problem (why is it important?). How are you are going to address it, abstractly specify how you will approach the problem carried in your report problem. 2. Problem definition Formally define the problem you are addressing. That is, formally specifying the inputs and outputs, but not necessarily in the context of the specific learning system you are using). 3. The learning system Describe the learning system you are going to employ by incorporating the following sub-sections. a. The target function Define and show the target function employed. b. Representation Give the representation of the input, such as the mapping from instances to input neurons in neural network, or chromosomes representation in genetic algorithms, or vectors of attributes in decision trees.
- 2. c. System structure For example in GAs - fitness functions, selection criterion, Crossover, mutation etc. For neural networks - how many layers, how many hidden units, activation functions, etc. d. The learning algorithm Specify and give an outline of the learning algorithm. Include a psuedocode description of the algorithm. If the algorithm was discussed in detail in class (such as the back- propagation algorithm), no need to write all the equations, but still give the general outline and the main equations. e. Improvements/modifications If you introduce modifications, variations or improvements, this is the place to describe them. For example if you employed special genetic operators for the genetic algorithm. 4. Experimental Evaluation/tests /Results Describe the experiments you did with the learning system in detail: a. The data sets Which data sets are you using to train your learning system? Type, size, source. If you are using train/test/validation set, how did you split the data between these sets? b. Learning How many runs? Did you play with the parameters (list parameter sets)? What is the stopping criteria (convergence? number of iterations? threshold accuracy?) Describe how you evaluated the performance of your system. Did you use validation set? test set? etc c. Results & Data analysis Give the quantitative results of your experiments using graphical data presentation such as graphs and histograms instead of confining yourself to tables and literature. Discuss your results presented here. What observations & conclusions can the results explain in terms of the underlying properties of the algorithm and/or the data.
- 3. What are the basic properties revealed in the data. If you are using neural network, do you have an interpretation of the weights (feature mapping)? If you are using GA and using various problem instances, is your GA problem instance dependent or independent? 5. Other learning systems If you are using more than one learning system, repeat briefly the steps 3 for the other learning systems you are using. 6. Comparing learning systems If experiments include a comparative study by using more than one learning system: What conclusions do you draw from the results about the strengths and weaknesses of one strategy over the other method(s)? Is one method uniformly superior to the other or it depends on the problem instance? 7. Concluding Remarks & Future work Give a brief summary of the important results and conclusions presented in the report. What are the most important points illustrated by your work? What are the major shortcomings of your current method? If possible, can you propose future additions or enhancements that would help overcome the shortcomings and improve on your work.