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Catalog Description

Course Objectives

Prerequisites

Class Location

Grading

Assignments

Additional Information

Schedule of Assignments and Events

Contact Information/Office Hours

Texts

• "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" (2nd ed.) by Aurelian Geron, O'Reilly, ISBN 9781492032649 (2019) (Required)
• PyTorch Tutorials
• Scipy Lecture Notes

Both this course and the Spring 2021 Statistical Machine Learning course will use the first (approximately) four chapters of Geron's book.

The Spring 2021 Statistical Machine Learning course, but not this course, will use the Scikit-Learn material from Geron's book.

Neither this course nor Statistical Machine Learning will use the TensorFlow material from Geron's book. This course will use PyTorch instead.

Software

• The Anaconda Python Distribution
• 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

• You will need a suitable computer. The University's Computer Recommendations are appropriate for this course for Windows and macOS. 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.
• TBD

Course Description

• Special Topics. Machine learning using artificial Neural Networks.
• This Online Learning course begins on August 18, 2020 at 8:00 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 December 8, 2020, 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 neural network machine learning techniques 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 of neural network machine learning techniques to a technical audience.

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Prerequisite(s)

• SSE 550 Object-Oriented Design I
• 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 (August 18 - December 8, 2020).
• 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 very 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 begin logging your time as specified on these pages, starting wherever you are in the course when you receive this notice (via Canvas). These logs will not be deliverables for this course.
• These reports are not deliverable.
• 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.

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

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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.
• Preparation: Learn to program in Python, roughly to the level of the Scipy Lecture Notes, sections 1 - 1.6, using Jupyter notebooks. This work is not deliverable.
• Project 1(Solo)
  • Demonstrate at least some basic capability to program/use PyTorch.
• Project 2 (Team)
  • Study chapters 1 - 3 of Geron, and read chapter 4. Take these contents into consideration in Projects 2 and 3.
  • Demonstrate more advanced (than Project 1) capability to program/use PyTorch.
• Project 3 (Team)
  • Demonstrate more advanced (than Project 2) capability to program/use PyTorch.

  Please raise questions and issues via Canvas.

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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?
• Login in to the course on Canvas before the second day of class.
• Study the assigned material.
• Write and read Canvas 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 messages about ideas, problems, projects, and solutions to do with the assigned homework.

Review the deliverables, which will be made available in the "Files" section of the course on Canvas, 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
August 18, 2020	December 8, 2020	Course in Progress
	August 18, 2020	Login in to the course on Canvas.
August 18, 2020	September 14, 2020	Work on Project 1
	September 14, 2020	Project 1 due
	October 12, 2020	Project 2 due
	December 8, 2020	Project 3 due Course ends

Notes: The course begins at 8:00 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:

"Disability Statement for Syllabus"

"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."

"Mental Health and Wellness Statement for Syllabus"

"Mental Health and Wellness: Mercer University faculty and staff recognize that mental health concerns can impact academic performance and interfere with daily life activities. Please notify your faculty member or academic advisor for academic assistance, as needed. CAPS can also provide support if you’re feeling stressed, overwhelmed, anxious, depressed, lost or are struggling with personal issues. Please call or visit the Counseling and Psychological Services (CAPS) website for more information. These services are free and confidential, and support non-traditional, graduate, and undergraduate students. Students at Regional Academic Center locations may call a CAPS Office for assistance in finding local services (that may charge for their services) if distance to either campus creates challenges. CAPS locations include: MACON – Linden House (attached to the MEP Residence Hall), 478-301-2862; ATLANTA – 215 Sheffield Student Center, 678-547-6060."

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Accreditation: Mercer University is accredited by SACSCOC.