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Schedule of Assignments and Events

Contact Information/Office Hours

Mercer University Coronavirus Website

Syllabus Statement for COVID Spring 2021 12.18.20-3

Mercer20_Protocols_12-14-20.pdf (COVID)

Texts

• Hands On Machine Learning with Scikit-Learn and Tensor Flow (2nd ed.) by Geron, Ingram/O'Reilly (2019) ISBN 9781492032649 (Required).
• Statistics for Machine Learning (1st ed.) by Dangeti, Packt (2017) ISBN 9781788295758 (Required).

Software

• Anaconda Python distribution, Individual Edition.
• Khan Academy math. Look for: "AP/College Statistics", and "Statistics and probability".
• General software requirements are described on the Online Learning page.
• All students who meet the prerequisites for this course will already have software development experience, and will therefore already be familiar with some software development environment. Students are welcome to use the environment of their choice, but if they wish suggestions, they are welcome to contact the professor.

Other Resources

• TBD
• You will need a suitable computer. The University's Computer Recommendations are appropriate for this course for Windows and OS X. If you use the Linux operating system this course, the recommendations for Windows computers will also be suitable for Linux.
• Please see the Online Learning page for more information regarding expected student student access to the internet, and corresponding software.
• Please see the Mercer's Distance Learning page for more information regarding expected student student access to the internet, and corresponding software.

Course Description

• This is a course in machine learning, statistical learning, and the appropriate application of statistics to both approaches.
• This Online Learning course begins on January 7, 2021 at 12:00 am Eastern time. All assignments are due by the end (11:59:00 pm) of the specified calendar day in the Eastern time zone. This course ends at 11:59:00 pm on May 3, 2021, in the Eastern time zone.

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Course Objectives

Breadth and Depth in Defining, Analyzing, and Solving Complex Problems:

• After successfully completing this course, you should be able to demonstrate a basic capability to apply statistical and machine learning techniques other than neural nets to solve complex problems.

Communicate Technical Aspects of the Solution for Complex Software Engineering Problems to a Technical Audience:

• After successfully completing this course, you should be able to demonstrate a basic ability to communicate regarding the application of statistical and machine learning techniques other than neural nets to a technical audience.

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Prerequisite(s)

• SSE 550 Object-Oriented Design I
• This prerequisite provides one graduate-level 3 semester hour course in C++ or Java or C#.
• Please see the Online Learning page for more information regarding expected student abilities and aptitudes.

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Class Location

• This course is entirely online.
• Please see the Canvas page for information regarding access to Canvas, Mercer's online learning system. Please note that the Canvas contents for this course are in addition to this syllabus, and do not substitute for this syllabus.

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Grading

Item	Percent of Term Grade
Project 1.	25
Project 2	35
Project 3	35
Canvas Discussion Contributions	5

• More information about project grading is contained in the General Project Rubric .

• Canvas Discussion Contributions are graded as follows:
• They must be substantive technical contributions, useful and informative to people in the course, on topic with the course material.
• They must be posted in Canvas while the course is in session.
• They must be at least six in number.
• Credit will be given only for original work.
• The Office of the Provost's "Academic Integrity page includes a link to the Graduate Honor Code, which covers issues such as plagiarism. Please take a good, careful look at the Graduate Honor Code. Plagiarism is not acceptable in this, or any, course.

• Project reports are to be submitted complete, not as incremental partial submissions.
• Instruction Time
• All course work time is categorized as either Direct or Non-Direct (but not both). More information about the Direct and Non-Direct categories is contained in the Direct and Non-Direct and Report Logs. Please read these pages carefully.
• The SACSCOC (our accrediting agency's) standard (minimum) for a three credit course such as this is 2250 minutes of direct activity and 4500 minutes of non-direct activity per semester. These minima add up to 6750 minutes of activity per semester.
• Please log your time as specified on this syllabus.
• These logs are not planned to be deliverable, and will not be graded. However, if questions arise regarding activity time, you may be asked to deliver your activity logs, although they will still not be graded.
• All deliverables are due on their assigned dates .
• Deliverables submitted no more than one day late may be graded, but with a 10 point penalty .
• Deliverables submitted more than one day (24 hours) late will receive a grade of zero.
• Manage your time and keep on schedule. You should schedule your work evenly throughout the term. Keeping data will help.

If you have a question about any of this, please ask.

Credit will be given only for original work.

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Assignments

• Purpose of projects:
• Use the projects to develop your capabilities, and
• Use the projects to demonstrate your capabilities.
• Deliverables for all projects:
• Projects shall be delivered via Canvas.
• Project reports should each be a single, standalone .pdf document, with the Bookmark feature used to support easy document navigation. Note that both MS Word and LibreOffice Write (which is free open-source software) support bookmarks, and will carry the bookmarks over into the pdf document when they convert your .docx, .odf, or whatever file type to PDF.
• Project reports should focus on the development and/or maintenance that you actually do. Reports should not focus on summarizing or explaining the text or other materials.
• Inclusion of screenshots demonstrating your work, such as showing test execution and results, is appropriate. But code must not be presented in screenshots. Code must be presented as text which can be searched using the "find" or "search" capability of the app (e.g., Acrobat Reader) that will be used to read your report. Long code listings can be placed in appendices if doing so does not interfere with you presenting your work on that code in the body of the text.
• Your report should be organized in such a way as to make the topics that you want credit for covering easy to find, and demonstrate your capabilities clearly and convincingly. Everything you want considered for credit, including code and tests (including test results), should be included in the report.
• Each project can be a single, integrated project that tries to actually do something, or a collection of exercises that demonstrate your capabilities but don't accomplish anything else, or any combination of these two alternatives that you find convenient, unless otherwise specified in the assignment.
• You may include other material outside of the report, if you wish, but this material may or _may not_ be considered in evaluating your work.
• For each capability that you demonstrate within a report or major section of that report, you may present only the final result of your work; you need not demonstrate every step in the development of that result.
• Use IEEE standards to cite references How to Cite References: IEEE Documentation Style. Careful attention to this will help you to avoid plagiarism.
• Some suggestions for doing a project are contained in the One Way to Do a Project page.
• You may arrange the teamwork pretty much as you please. The team members should develop a single deliverable report.
• Programming Preparation (solo or team):
• Install the Anaconda Python distribution (version 3 of Python).
• Learn to develop Python code using Jupyter notebooks.
• Study the Scipy Lectures ch. 1 - 4.2, 5 - 5.3, and 16 - 16.1. You may well want to come back to this resource as the course progresses, to cover other topics.
• This preparation is not deliverable, and will not be graded.
• Project 1 (team):
• Study chapters 1 - 4 of Geron. You will need to apply the ideas of chapters 1 - 3 throughout this course. This study is not deliverable (do not reflect it in your report).
• Do your own project demonstrating regression and logistic regression, using machine learning techniques (per Geron). Do not reuse the examples from our textbooks or other resources. These projects (regression and logistic regression) are deliverable, and constitute the graded portion of Project 1.
• Start early. This is more challenging than you think!
• Statistical Preparation (solo or team):
• Study ch, 1 of Dangeti. If you are not already proficient in these topics (e.g., from EGR 252), then other resources, such as the Khan Academy or other online learning materials may be useful.
• This preparation is not deliverable, should not be included in your report, and will not be graded.
• Project 2 (team):
• Study chapter 2 - 3 of Dangeti.
• Apply that material from Dangeti to a new regression project, and a new logistic regression project. Random Forests are not part of this assignment.
• Project 3 (team):
• As with Projects 1 and 2, apply the ideas of Geron ch. 1 - 3 as appropriate.
• Develop and demonstrate your capabilities with additional ML and/or statistical learning projects, such as: SVM's, Decision Trees, Random Forests, Ensemble Learning, PCA, K-Means, K-Nearest Neighbors, etc.
• Please raise questions and issues via Canvas Discussions.

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Additional Information

• Asynchronous Learning Nets (ALNs)
• • Self Study: You study the texts and any other course study material on your own.
  • Collaborative Projects: After your self-study, you collaborate (communicating via whatever means the team members find acceptable) with your partner(s) to produce a team product. Your collaborative work is substantial, asynchronous, and rapid.
  • This course is an online learning course. Please read this Online Learning web page regarding online learning in this course and this program.
• What Do I Do?
• Study the assigned material.
• Write and read email messages about ideas, problems and solutions to do with the assigned study material.
• Do the assigned work, deliver the assigned deliverables.
• Write and read Canvas discussions about ideas, problems, projects, and solutions to do with the assigned homework.
• Review the deliverables produced by other people.

Changes to this Syllabus:

There will be changes to this syllabus, so check back frequently, and don't forget to hit "Reload" or "Refresh".

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Schedule of Assignments and Events (tentative)

Start Date	End Date	Activity/Event
January 7, 2021	May 3, 2021	Course in Progress
	January 7, 2021	Login in to the course on Canvas.
January 7, 2021	February 8, 2021	Work on Programming Preparation (as needed), and on Project 1
	February 8, 2021	Project 1 due
February 9, 2021	March 8, 2021	Work on Statistical Preparation (as needed), and on Project 2
	March 8, 2021	Project 2 due
	May 3, 2021	Project 3 due Course ends

Notes: The course begins at midnight (12:00 am) on the first day of class (see schedule above), and ends at 11:59:00 pm on the last day of class. Assignments are due at any time during the day specified on the schedule, that is, before 11:59:00 pm on that day. All times are Macon, Georgia (Eastern time zone) times.

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Contact Information

• My Name: Dr. Paul E. MacNeil
• My E-mail: macneil_pe@mercer.edu .
• OfficeHours
• My Office Phone: 478 301-2185
• US Mail:
 
• Dr. Paul E. MacNeil
• School of Engineering
• Mercer University
• 1501 Mercer University Drive
• Macon, GA 31207

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Important Additional Information:

Zoom Office Hours:

Virtual office hours will be conducted synchronously via Zoom. During Zoom office hours you must follow appropriate protocols in terms of audio muting and video enabling as directed by the instructor. Zoom sessions are a professional environment, you must ensure that your username, demeanor, dress, and background are appropriate.

"Firearms/weapons are not allowed in Mercer University campus or Regional Academic Center (RAC) facilities except when required for students who are in uniform (police, law enforcement officers, etc.) in an official duty status. All other weapons are prohibited including, but not limited to, hunting weapons, BB guns, stun guns, air rifles, air pistols, paintball guns, bowie knives, daggers, dirks, slingshots, leaded canes, metal knife sharpeners, switchblade knives, blackjacks, metallic knuckles, and any sharp, pointed or edged instrument (except instructional supplies, unaltered nail files and clips, and tools used solely for preparation of food, instruction, and maintenance). All fireworks or explosives (defined as any substance prepared for the purpose of producing visible or audible effects by combustion, explosion, or detonation) are forbidden. https://police.mercer.edu/

“Students in need of accommodation due to a disability should contact the Access and Accommodation Office to complete the verification process to become approved for services. In order to receive accommodations, each term, students will request accommodation and faculty notification forms through the Access Office online system Accommodate. Students are strongly encouraged to schedule a meeting with each professor in a timely manner to discuss arrangements. Accommodations are not retroactive in nature. Note - Disability accommodations or status are not reflected on academic transcripts. Students with a history of a disability, perceived as having a disability or with a current disability who do not wish to use academic accommodations are also strongly encouraged to complete the verification process with the Access Office. Students must request accommodations in a timely manner to receive accommodations in a timely manner. The Access Coordinator for Macon Campus is Katie Johnson, Director and ADA/504 Coordinator. Phone: (478) 301-2778; email: johnson_kc@mercer.edu Website: https://access.mercer.edu."

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Accreditation: Mercer University is accredited by SACSCOC.