CPSC 558 401, Spring 2026 Syllabus

General Information

Instructor:

Dr. Dylan Schwesinger

Office:

Old Main 250; Phone: (484) 646 - 4389

email:

schwesin@kutztown.edu

Web URL:

https://faculty.kutztown.edu/schwesin

Office hours:

Monday 3:30pm – 5:30pm

Tuesday and Thursday 9:30am – 11:00am

Meeting Time & Place:

Wednesday 6:00pm – 8:50pm, OM 158

Course Description:

This course covers advanced study and practice in data mining and predictive analytics. Topics include understanding, configuring, and applying advanced variants of data association, classification, clustering, and statistical analysis engines, analyzing and applying underlying machine learning algorithms, exploring instance-based, support vector, time-series, ensemble, graphical, and lazy learning algorithms, meta-learning, neural nets, genetic algorithms, and validating results. The course examines topics specific to very large data sets. Data cleaning and formatting require some programming in a modern scripting language. Other course activities include using, extending, and customizing off-the-shelf machine learning software systems to accomplish the tasks of data analysis.

Prerequisite: CPSC 458 or Unconditional acceptance to the graduate program.

Textbook (Recommended): Data Mining: Practical Machine Learning Tools and Techniques, 5th Edition_, Witten et. al.

Course Objectives

• Custom-configure standard association, classification, statistical, numerical, and/or clustering algorithms to achieve more accurate and reliable results in working with data sets.

• Apply unsupervised and supervised data transformations to improve the results of analysis.

• Apply two or more of instance-based learning, other lazy learning algorithms, support vector machines, time-series data analysis, ensemble learning, graph model learning, meta-learning, neural nets, Markov Models, and/or genetic algorithms, to solve data analysis problems using an extensible tool set.

• Apply the map-reduce algorithm, or another applicable algorithm, to analyze a very large data set.

• Describe research areas in machine learning and its application of data analysis.

• Specify the results of analysis via written summaries, graphical illustrations, and/or proposed automated mechanisms.

Course Organization

Your participation in the course will involve the following activities:

• Attending the lectures
• Doing assignments

Regular attendance and class participation are expected. Students are responsible for all material covered in class.

Policies

Assignments

All assignments are due by the specified day and time. Late assignments will not be accepted. All assignments must include the following information: your name, the course (CPSC 558), semester, year, and assignment number. Programming assignments must follow the Computer Science Documentation Standards. Failure to meet these expectations will result in a 10% penalty for that assignment.

Final Grade Assignment

Each student will receive a numeric score for the course based on a weighted average of the following:

• Assignments (100%): There will be several assignments which combined will count for 100% of the course grade. Assignments may have different weights based on the perception of the relative effort required.

The letter grade cutoff points are 93 (A), 90 (A-), 87 (B+), 83 (B), 80 (B-), 77 (C+), 70 (C).

Bonus Points: The instructor will selectively consider raising individual grades for students just below the cutoffs based on factors such as attendance, class participation, improvement throughout the course, and special circumstances.

Academic Dishonesty

All students should familiarize themselves with the Computer Science Academic Integrity Policy

Assignments will be closely monitored for plagiarism. All infractions will be reported to the department chair. The penalty for cheating will be determined on a case-by-case basis, but it will always be worse than having not turned in the assignment.

Email Correspondence

The preferred method of course communication is email. When sending email, please indicate the course number in the subject line by placing it within square brackets, for example, “[CPSC 558] Need help on Assignment 1”. All email correspondence must sent from your Kutztown University email address. You can expect a response to an email with a properly formatted subject line within 24 hours. An email with an improperly formatted subject line may get no response at all.

Classroom Etiquette

Consideration for your classmates, instructor, and class is expected. Please come to class on time and prepared to learn. There should be no classroom conversations, sleeping, cell phone usage, or other disruptions to the class.

Accreditation

Any course work submitted to the instructor (including but not limited to assignments, tests, and projects) may be photocopied and retained for the purpose of assessment, accreditation and quality improvement, after removal of any information identifying the student.

Kutztown University Class Handout Information

Supplemental information from university offices for class handouts and syllabi is located here: Class Handout Information

Note: This syllabus is subject to change at the discretion of the instructor.