A special-topics course discussing data mining and machine learning algorithms for analyzing and data visualization techniques for visualizing very large amounts of data. The course emphasizes MapReduce as a tool for creating parallel algorithms that can process very large amounts of data. Programming assignments are completed in one or more high-level languages.
Students will be introduced to data mining, machine learning, and data visualization techniques. Students will use both descriptive and predictive methods on non-trivial data sets. Students will become familiar with various modalities of data and the challenges inherent with each.
This course provides additional exposure to database, machine learning, distributed/parallel computing, and algorithmic methods. In this course, students will expand their skills in each of these areas.
The course will include regular homework and/or programming assignments. There will be no credit given for late assignments (without an excused absence)—turn in as much as you can. Unless otherwise specified, no handwritten work will be accepted.
Reading should be completed before the lecture covering the material per the provided schedule. Not all reading material will be covered in the lectures, but you will be responsible for the material on homework and exams. Quizzes over the assigned reading may be given at any time.
See the GFU CS/IS/Cyber policies for collaboration and discussion of collaboration and academic integrity. Most students would be surprised at how easy it is to detect collaboration or other academic integrity violations such as plagiarism in programming—please do not test us! Remember: you always have willing and legal collaborators in the faculty. We encourage you to ask questions in class, ask for help in the CS lab, use the class mailing list, and visit office hours for assistance.
Unless otherwise specified (e.g., for a group assignment or project), you are expected to do your own work. This also applies to the use of online resources (e.g., StackOverflow, ChatGPT). Put simply: if you are representing someone else's work as your own, you are being dishonest. Any suspected incidents of academic integrity violations will be investigated and reported to the Academic Affairs Office as they arise.
Almost all of life is filled with collaboration (i.e., people working together). Yet in our academic system, we artificially limit collaboration. These limits are designed to force you to learn fundamental principles and build specific skills. It is very artificial, and you'll find that collaboration is a valuable skill in the working world. While some of you may be tempted to collaborate too much, others will collaborate too little. When appropriate, it's a good idea to make use of others—the purpose here is to learn. Be sure to make the most of this opportunity but do it earnestly and with integrity.
If you have specific physical, psychiatric, or learning disabilities and require accommodations, please contact Disability & Accessibility Services (DAS) as early as possible so that your learning needs can be appropriately met. For more information, go to georgefox.edu/das or contact das@georgefox.edu).
My desire as a professor is for this course to be welcoming to, accessible to, and usable by everyone, including students who are English-language learners, have a variety of learning styles, have disabilities, or are new to online learning systems. Be sure to let me know immediately if you encounter a required element or resource in the course that is not accessible to you. Also, let me know of changes I can make to the course so that it is more welcoming to, accessible to, or usable by students who take this course in the future.
The Academic Resource Center (ARC) on the Newberg campus provides all undergraduate students with free writing consultation, academic coaching, and learning strategy review (e.g., techniques to improve reading, note-taking, study, time management). The ARC offers in-person appointments; if necessary, Zoom appointments can be arranged by request. The ARC, located on the first floor of the Murdock Library, is open from 1:00–10:00 p.m., Monday through Thursday, and 12:00–4:00 p.m. on Friday. To schedule an appointment, go to the online schedule at traccloud.georgefox.edu, call 503-554-2327, email the_arc@georgefox.edu, or stop by the ARC. Visit arc.georgefox.edu for information about ARC Consultants' areas of study, instructions for scheduling an appointment, learning tips, and a list of other tutoring options on campus.
George Fox University uses a robust referral and support system, Fox360, to learn about students who are experiencing various student success concerns. Students who are referred by a professor, other employee, or fellow student will be contacted by a member of our Student Support Network to explore the student's situation, develop a plan, and connect with relevant campus resources. GFU community members who have a concern about a student's well-being can submit an aleart by going to fox360.georgefox.edu. Our goal is to provide 360° care for students as they navigate their college experience. For more information see ssn.georgefox.edu or contact Rick Muthiah, Director of Learning Support Services.
Please review the entirety of the university's official COVID-19 web page for the most up-to-date community guidance.
The final course grade will be based on:
Week 1Data Mining and MapReduce
Reading: Ch. 1–2 |
Week 2Finding Similar Items
Reading: Ch. 3 |
Week 3Data Streams
Reading: Ch. 4 |
Week 4Link Analysis
Reading: Ch. 5 |
Week 5Frequent Itemsets
Reading: Ch. 6 |
Week 6Clustering
Reading: Ch. 7 |
Week 7Web Advertising
Reading: Ch. 8 |
Week 8Data Visualization
|
3/3Midterm exam
Reading: Ch. 1–8 |
Week 9Recommendation Systems
Reading: Ch. 9 |
Week 10Communities
Reading: Ch. 10 |
Week 11Spring break — no class
|
Week 12Dimensionality Reduction
Reading: Ch. 11 |
Week 13–14Machine Learning
Reading: Ch. 12 |
Week 15Institutional Analytics
|
4/26Final exam
Reading: Ch. 1–12 |
This page was last modified on 2020-07-30 at 18:28:28.
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