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.
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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Additional Information
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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