| Course Location | Meeting Days | Time |
|---|---|---|
| TBD | Tuesdays & Thursdays | 9:30-11:30am |
| Instructor Information | Office Location | Hours |
|---|---|---|
| Dr. Jason S. Byers | Main Office Chambers 2256 |
TBD |
| jabyers@davidson.edu (704) 894-2760 |
Data CATS, drop in hours Hurt Hub |
TBD |
Course Home
Everything you need for this class
(announcements, resources, assignments and other activities) will be
posted on the course website. Please
plan to check the page regularly.
Course Meeting Link
TBD
Course Format
This is an online course with
both synchronous and asynchronous components. Here’s what that means in
practice: each week, you will be assigned a lab to work through. Each
lab will typically comprise a set of readings/videos and a coding
exercise to be completed. You will then meet with me via Zoom once a
week to discuss the readings/videos and the coding exercises on Thursday
9:30 am - 11:30 am. Follow-up meetings may be scheduled as necessary on
Tuesdays 9:30 am - 11:30 am, EST.
Course Description
Machine learning is the
subfield of Artificial Intelligence that is concerned with the problem
of designing algorithms and systems that improve their performance in a
certain task with accumulated experience. While the ability to learn is
clearly a key trait that any system attempting to behave “intelligently”
must possess, machine learning techniques have increasingly become
central to many software systems. For example, learning algorithms are a
fundamental component of state-of-the-art systems for filtering spam,
detecting fraud, recommending products to purchase, and understanding
visual and textual content. This course will introduce students to some
of the fundamental algorithms in this field, the theory that underpins
these approaches, and the practicalities of applying these ideas to
novel, real-world problems. Topics that will be covered include
techniques for regression (linear, logistical, polynomial),
classification algorithms (k-nearest neighbors, decision trees,
support vector machines), the bias-variance decomposition, ensemble
methods (bagging, boosting), and dimensionality reduction
techniques.
Learning Outcomes
Together, we will strive for
your individual and collective success in achieving the learning
outcomes of this course. At the conclusion of this course, students will
be able to:
Prerequisites
MAT 105 (or an equivalent) or by
permission of the instructor
Course Materials
To maximize access to this
class, we will use freely available textbooks, videos, and other
resources, with a focus on the following:
Primary text (ISL): James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning with Applications in R. Second Edition. New York: Springer. This book is freely available online. It is also available in paperback, if you prefer a hard copy. Warning: some content and the numbering system differs between print and online versions; I will exclusively refer to the free online version.
Supplementary text (HOML): Boehmke, Bradley, and Brandon Greenwell. 2020. Hands-On Machine Learning with R. New York: Chapman and Hall/CRC. This book is freely available online. It is also available in paperback, if you prefer a hard copy. Warning: some content and the numbering system differs between print and online versions; I will exclusively refer to the free online version.
Software
You will use two freely available
programs, R and RStudio, in order to complete the assignments for this
course. R and RStudio are installed on all Davidson campus computers.
They are also freely available to install on your own computer.
Access and Accommodation
The college welcomes
requests for accommodations related to disability and will grant those
that are determined to be reasonable and maintain the integrity of a
program or curriculum. To make such a request or to begin a conversation
about a possible request, please contact the Office of Academic Access
and Disability Resources, which is located in the Center for Teaching
and Learning in the E.H. Little Library: Beth Bleil, Director, bebleil@davidson.edu, 704-894-2129; or Alysen Beaty,
Assistant Director, albeaty@davidson.edu, 704-894-2939. It is best to
submit accommodation requests within the drop/add period; however,
requests can be made at any time in the semester. Please keep in mind
that accommodations are not retroactive.
Course Organization
Modes of learning in this
class (whether assessed directly or indirectly) require a range of
skills and abilities. Every student’s success is important to me, and I
am happy to work with you to develop strategies for success in this
class. For Summer 2021, we will be meeting remotely, to allow everyone
to participate fully in the collaborative environment that is necessary
to maximize your learning.
In-Class Activities. Each class day will involve a significant amount of discussion of the readings and topics for that week. Additionally, the class meeting will be a time for students to demonstrate their applied knowledge of the readings and topics for each week. In order for these activities to be effective, you must do the assigned readings and videos before you come to class, and be prepared to ask (and answer) questions before diving into a discussion.
Labs. Weekly assignments (due approximately every Thursday at 9:30 am EST, for a total of 10 labs) will provide you with regular practice applying machine learning techniques in R. These assignments will build on the material presented in class, and require you to apply the concepts in new ways.
Final Project. The goal of the final project is for you to apply the machine learning techniques and skills learned in this course to real data.
Attendance Policy
Missing class will adversely
affect your grade in many ways. In addition, the college attendance
policy will be enforced: missing more than 25% of class meetings makes
you eligible for a failing grade. Please look carefully at the syllabus
during the first week of class. Should there be a conflict between any
class session or assignment due date and a religious holiday or
observance, athletic contest, or another academic or personal commitment
please let me know well in advance. Religious observance warrants a
legitimately excused absence. If you must miss class for any reason,
excused or otherwise, you are responsible for getting notes from a
classmate and turning in all work on time. Each student will be granted
2 unexcused absences.
Getting Help
It is normal and expected that all
students will need help outside of class with the material in this
course. Because a language like R is only learned with practice, an
important source of help is additional exercises, in the required
textbook or optional online resources provided on the course web page.
The following additional resources are also available.
Office Hours. I welcome you to visit me during the hours listed at the beginning of this document. It is a good practice to make an appointment with me even if outside of the listed hours of availability.
Reusing/Sharing Code. Many of the datasets we will discuss and analyze are publicly available, so they may have been extensively discussed and analyzed. Unless explicitly instructed otherwise, you may use available code and resources for course activities (e.g., Github repos, StackOverflow answers) but you must cite the source of the code/resource within your program files and/or document. Recycled code that is discovered that is not properly cited may be considered as plagiarism. When working in groups on class assignments you are welcome to discuss problems together and ask for general advice, but you may not share or use code from another group.
Honor Code. Please adhere to the Davidson College Honor Pledge.
Grading
| Category | Points |
|---|---|
| Attendance | 10 Points |
| Participation | 10 Points |
| Labs | 60 Points |
| Final Project | 20 Points |
A tentative class schedule of topics, readings and due dates is
available below. Minor adjustments will be made as needed, on the course
web page. Please double check the web page before doing each reading
assignment.
Topics
Date \(~~~~\) Readings
6/2 \(~~~~\) ISL Chapter 1 & ISL Chapter
2
\(~~~~~~~~~~\) Class Notes I
\(~~~~~~~~~~\) Class Notes II
Assignments
\(~\) Lab 1
\(~\) Lab 1 Guide
Topics
Date \(~~~~\) Readings
6/9 \(~~~~\) ISL Chapter 3
\(~~~~~~~~~~\) Class Notes
Assignments
\(~\) Lab 2
\(~\) Lab 2 Guide
Topics
Date \(~~~~\) Readings
6/16 \(~~~~\) ISL Chapter 4
\(~~~~~~~~~~\) Class Notes
Assignments
\(~\) Lab 3
\(~\) Lab 3 Guide
Topics
Date \(~~~~\) Readings
6/23 \(~~~~\) ISL Chapter 5
\(~~~~~~~~~~\) Class Notes
Assignments
\(~\) Lab 4
\(~\) Lab 4 Guide
Topics
Date \(~~~~\) Readings
6/30 \(~~~~\) ISL Chapter 6
\(~~~~~~~~~~\) Class Notes
Assignments
\(~\) Lab 5
\(~\) Lab 5 Guide
Topics
Date \(~~~~\) Readings
7/7 \(~~~~\) ISL Chapter 7
\(~~~~~~~~~~\) Class Notes
Assignments
\(~\) Lab 6
\(~\) Lab 6 Guide
Topics
Date \(~~~~\) Readings
7/14 \(~~~~\) ISL Chapter 8
\(~~~~~~~~~~\) Class Notes
Assignments
\(~\) Lab 7
\(~\) Lab 7 Guide
Topics
Date \(~~~~\) Readings
7/21 \(~~~~\) ISL Chapter 9
\(~~~~~~~~~~\) Class Notes
Assignments
\(~\) Lab 8
\(~\) Lab 8 Guide
Topics
Date \(~~~~\) Readings
7/28 \(~~~~\) ISL Chapter 10
\(~~~~~~~~~~\) Class Notes
Assignments
Topics
Date \(~~~~\) Readings
8/4 \(~~~~\) ISL Chapter 12
\(~~~~~~~~~~\) Class Notes
Assignments
\(~\) Lab 10
\(~\) Lab 10 Guide
Topics
Date \(~~~~\) Readings
8/11 \(~~~~\) Final Project
Assignments