Course Information
| Location: | EnGeo 2209 |
| Meeting Time: | T/TH 11:10AM-12:25PM |
| Prerequisites: | A grade of "C-" or better in CS 327 and one of the following: MATH 318, MATH 220 or MATH 229. |
| Course Web Page: | http://w3.cs.jmu.edu/spragunr/CS445/ |
| Required Textbook: | Introduction to Data
Mining (Second Edition), Pang-Ning Tan et. al. Pearson, 2019. |
Instructor Information
| Name: | Dr. Nathan Sprague |
| Office: | King Hall 226 |
| Office Phone: | 568-3312 |
| Email: | spragunr@jmu.edu |
| Office Hours: | http://w3.cs.jmu.edu/spragunr/schedule.html |
Questions related to course content should be asked through Piazza. You may use email if you need to contact me directly.
You are welcome to call or stop by my office any time, with the understanding that I may or may not be available outside of my posted office hours. If you need to meet with me outside of my posted hours, email me to make an appointment.
Note that I typically do not respond to email or Piazza posts in the evenings or on the weekends.
If you contact me and don't hear back within a day or two, don't give up! I try to respond to email in a timely manner, but sometimes one falls through the cracks. I won't be offended if you send a follow up message.
My office hours this semester will use a hybrid online/in-person format. You are free to attend in-person or online via Zoom. The Zoom link will be made available on the course Canvas page.
Course Description
In this course, you will be introduced to machine learning, a class of techniques which involve algorithms that "learn by example".
Machine learning algorithms can be used to solve problems that would be difficult or impossible to solve using hand-written code. For example, imagine writing a traditional program to decode a zip code from a picture of an envelope. Machine learning uses labeled examples of hand-written zip codes (digits) and "learns" from these examples to recognize/decode zip codes from millions of pieces of mail each day.
This course focuses on a balance of theoretical and practical knowledge. Popular methods such as neural networks, deep learning, and support vector machines will be covered. Small projects will allow students to apply these techniques and communicate results in a quantitative manner.
Course Goals
At the conclusion of this course students should:
- Be able to translate from algorithmic descriptions to working implementations of key machine learning algorithms.
- Be able to apply machine learning techniques to solve practical problems. Be able to select an appropriate algorithm for a particular task.
- Be able to appropriately use validation and test data to tune and evaluate machine learning algorithms.
- Be able to locate scholarly sources of information related to machine learning. Read, summarize, and present information from those sources.
- Gain familiarity with key areas of machine learning including:
- Classification
- Regression
- Clustering
- Dimensionality Reduction
- Anomaly detection
Course Content and Preliminary Schedule
A detailed schedule, including reading and homework assignments, is available on the course web page. That schedule will be updated throughout the semester and should be checked regularly.
Methods of Evaluation
Course grades will be based on two midterms and a final exam, in-class activities, homework assignments, projects, and occasional quizzes. Projects will include 4-5 programming assignments and a poster presentation. Assignment specifications and due dates will be posted to the course schedule page. The final grade will be computed as follows:
| In-Class Activities | 15% |
| Reading Quizzes and Muddiest Points submissions | 5% |
| Projects and Homework Assignments | 20% |
| Final Poster Project | 10% |
| Midterms | 30% |
| Final Exam | 20% |
Letter grades will be assigned on the scale A=90-100, B=80-89, C=70-79, D=60-69, F=0-59, with potential minor adjustments after considering the overall performance of the class and actual distribution of numeric scores. I will use "+" and "-" grades at my discretion. I do not assign WP or WF grades except under extraordinary circumstances.
Muddiest Points
Most weeks I will ask you to submit a "muddiest point" reflection highlighting concepts that were unclear from the reading or lecture.
Course Policies
Attendance and Participation
Regular attendance and fully engaged participation is expected. Your grade will be partially based on in-class assignments and quizzes, so attendance will affect your grade.
Academic Integrity
Your work in this course must comply with the provisions of the JMU honor code: http://www.jmu.edu/honor/code.shtml. It is not a violation of the honor code to discuss assignments with other students at a conceptual level. However, all of the work that you submit must be developed by you, based on your own understanding of the material. Representing someone else's work as your own, in any form, constitutes an honor code violation. It is also a violation of the honor code to "render unauthorized assistance to another student by knowingly permitting him or her to see or copy all or a portion of an examination or any work to be submitted for academic credit." In the context of this course, this portion of the Honor Code means:
- Copying and/or sharing code is expressly forbidden. This includes copying code from previous semesters' solutions (including your own or other students') or other external sources. It also includes posting your code publicly, such as on Github. Note that "copying" may refer to any mechanism of transferring solutions from one individual to another. If you "talk through" your solution with another student to the point that their code is clearly a line-for-line duplicate of yours, this is effectively copying even if you never explicitly provided access to your own file.
- It is acceptable to consult other resources (e.g., Stack Overflow) for clarifying examples but not wholesale copying of significant pieces of code. All such references must be documented explicitly within code comments.
- You are free to use ChatGPT, Copilot and other AI-enabled tools for your work in this course.
A key component of academic integrity is giving credit where credit is due. If you receive assistance, either from another student, from an AI-tool, or from some other source, you must provide a clear and explicit acknowledgment of that assistance at the time of your submission.
I will prosecute honor code violations if they come to my attention. If in doubt about what is allowed, ask me.
Missed and Late Assignment Policy
If you are unable to take an exam at the scheduled time because of illness or other problems, you must contact me beforehand to arrange to take the exam at a different time. Failure to make prior arrangements for a missed exam will result in a grade of 0 for the exam.
In general, attendance will be required in order to receive credit for in-class work. In the case of an unavoidable absence send me an email, in advance, with a brief explanation of why you can't attend. I may not respond immediately, but your email will provide a record of the circumstances. You will not receive credit for in-class work if no prior notice is provided. I will do my best to accommodate occasional absences, but you will not receive credit if you show a chronic pattern of missed classes.
Homework and Programming assignments will generally be due at 11:59PM on the posted due-date. Assignments submitted after the deadline will be subject to a 25% penalty for the first day. The penalty will increase by 25% every 24 hours. No assignments will be accepted more than 48 hours after the deadline.
Except under extraordinary circumstances, I will not provide extensions for illnesses, extracurricular obligations, etc. Instead, you will have three "late days" which may be applied to any project or homework assignment (with some exceptions for assignments due at the end of the semester or immediately before exams). The use of a late day allows you to extend the deadline for an assignment by 24 hours. You may apply multiple late days to a single assignment, but they may not be used to submit assignments more than 48 hours late.
Classroom Behavior
Phones should be silenced and put away during class unless they are being used for a course-related activity. If you violate this policy, I reserve the right to give you a long pointless lecture about the failings of your generation and the probable downfall of civilization. This will be embarrassing for both of us.
Please be ready to begin work at the scheduled start of class and refrain from packing up to leave until the class period is over. In exchange, I will commit to ending class promptly at the schedule time.
Adding/Dropping
Students are responsible for adding and dropping courses via MyMadison. Please consult the registrar's page of dates and deadlines for exact deadlines.
Disability Accommodations
If you need an accommodation based on the impact of a disability, you should contact the Office of Disability Services (Student Success Center, Room 1202, www.jmu.edu/ods, 540-568-6705) if you have not previously done so. Disability Services will provide you with an Access Plan Letter that will verify your need for services and make recommendations for accommodations to be used in the classroom. You are encouraged to with me to review the course requirements and your requested accommodations to develop an individualized plan appropriate for this course.
Inclement Weather Policy
This class will operate in accord with JMU's inclement weather policy available at http://www.jmu.edu/JMUpolicy/1309.shtml
Religious Observation Accommodations
I will give reasonable and appropriate accommodations to students requesting them on grounds of religious observation. If you require such accommodations you must notify me at least two weeks in advance.
Acknowledgments
Some of the course content this semester (including portions of this Syllabus) was originally developed by Kevin Molloy.