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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

• Scipy Lecture Notes (Required).
• Scikit-Learn Examples (Required).
• The Wikipedia page on Machine Learning (Required).

Software

• The Anaconda Python Distribution (version 3.X). Be sure to get Python version 3.X (3.7 at the time this is written), not version 2. (Required). You can look at the " Moving to require Python 3" page. Anaconda includes Python, Scikit-Learn, Ipython, Jupyter, and pretty much all of the other programming software that you will need.
• 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 techniques other than neural nets/deep learning.

This Online Learning course begins on August 20, 2019 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 December 10, 2019, 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 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 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	40

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.

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

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:

  All course work time is categorized as either Direct or Non-Direct (but not both). Read Direct and Non-Direct and Report Logs before continuing. Please read these pages very carefully.

  • 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 or as a Jupyter notebook. For this course, Jupyter notebooks are preferred, but there will be no penalty for using the PDF option. Note that both MS Word and LibreOffice (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, and code you actually wrote for the project, are appropriate. 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.
• All team members must be registered for credit in this course. You may arrange the teamwork pretty much as you please , but each team member must primarily make substantive contributions to the code; review contributions are not enough. The team members may develop a single report, etc., or multiple deliverables, one per member. In this latter case, team members should perform substantive peer reviews of each others's work as that work progresses, to help their team mates to do better work. Persuasive evidence of these reviews should be included in the reports.
• Project 1 (solo):
• Read the Wikipedia page on Machine Learning. Do not "hang up" trying to master all of it, but do emphasize: supervised learning, unsupervised learning, decision trees, and support vector machines.
• Install your development environment (Anaconda).
• Start learning to use your development environment:
• As you start learning Python (below), do at least a very few programs at the Python prompt (>>>) so that you understand what it is.
• The switch to the Ipython console (In [1]:), so that you know what it is.
• Then switch to Jupyter notebooks Jupyter notebooks. Note that Jupyter, Ipython, and all the other software you need is included in Anaconda. There is a little bit of learning curve here, but Jupyter will raise your productivity enough to more than pay back the learning curve.
• Start learning Python and the necessary related libraries from the Scipy Lecture Notes. The following sections are specifically needed: Beginning through 1.3.2.2; 1.4.1 - 1.4.3, and at least some of 1.4.4; and 1.5 (quick look only); 3.1 (at least a little). Expect to come back to this material, including some not covered in this list, as we progress through the course.
• Demonstrate your learning by including your original work in your Jupyter notebook report.
• Project 2 (team):
• Study and develop your capabilities in section 3.6 ("scikit-learn: machine learning in Python") of the Scipy Lecture Notes
• Demonstrate your learning by including your original work in your Jupyter notebook report.
• Project 3 (team):
• Study and develop your capabilities in the Scikit-Learn Examples.
• Demonstrate your learning by including your original work in your Jupyter notebook report.
• Use of data sets that are not included with the Python libraries is preferred.
• Please raise questions and issues via the course MS Teams team.

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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.

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• 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 email messages (to/from the course contact group) 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".

Schedule of Assignments and Events (tentative)

Start Date	End Date	Activity/Event
August 20, 2019	December 10, 2019	Course in Progress
	August 20, 2019	Login in to the course on Canvas.
January 7, 2019	September 16, 2019	Work on Project 1
	September 16, 2019	Project 1 due
	October 21, 2019	Project 2 due
	December 10, 2019	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 .
• My Office Phone: 478 301-2185
• US Mail:
 
• Dr. Paul E. MacNeil
• School of Engineering
• Mercer University
• 1501 Mercer University Drive
• Macon, GA 31207
• Office Hours

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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. S tudents 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.