Classes: MW 5:55pm - 7:10pm, Maria Sanford Hall 108
Instructor: Dr. Zdravko Markov, MS 30307, (860)-832-2711, http://www.cs.ccsu.edu/~markov/
Office hours: MW 4:30pm - 5:45pm, TR 1:30pm - 3:00pm, in person. Book an appointment here.
Catalog description: Machine Learning is the study of computational paradigms that allow computers to find patterns and regularities in data, perform prediction and forecasting, and generally improve performance through interaction with data. The course covers fundamental machine learning methods for data preprocessing, knowledge representation and visualization, classification, prediction, and clustering. Important applications of Machine Learning as data mining, text and web mining are also discussed. Students will use current machine learning software for hands-on exercises and projects.
Course Prerequisites: CS 501 or admission to the Artificial Intelligence or Software Engineering MS programs.
Prerequisites by topic
Required textbook: James Foulds, Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal. Data Mining: Practical Machine Learning Tools and Techniques (5th Edition), Morgan Kaufmann, 2025, ISBN 9780443158889.
Recommended textbook: Y. S. Abu-Mostafa, M. Magdon-Ismail, and H-T. Lin. Learning From Data, AMLbook.com, March 2012.
Required software: The Weka Workbench - open-source machine learning software available at https://ml.cms.waikato.ac.nz/weka/index.html
Class Participation: Active participation in class is expected of all students. Regular attendance is also expected. If you must miss a class, try to inform the instructor of this in advance. In case of missed classes and work due to plausible reasons (such as illness or accidents) limitted assistance will be offered. Unexcused absences will result in the student being totally responsible for the make-up process.
Course Expectations for Out-of-Class Work: To succeed in this 3-credit class, it is expected that you commit a total of 12 hours per week to master the course material. This includes 2.5 hours of lecture time and an additional 9.5 hours dedicated to independent study and coursework. This time commitment aligns with the expectations set by the Computer Science department for major courses and adheres to university policies. Recognizing that dedicating this amount of time outside the classroom is a significant commitment, it is nevertheless necessary for success. Please plan your course load accordingly.
Grading: Grading will be based on five homework assignments (50%), a midterm test (25%), and a final exam (25%). The assignments and tests are listed in the tentative schedule of classes below and will be made available and submitted via Blackboard. The letter grades will be assigned according to the following table:
Unexcused late submission policy: Submissions made more than two days after the due date will be graded one letter grade down. Submissions made more than a week late will receive two letter grades down. No submissions will be accepted more than two weeks after the due date.
Honesty policy: The CCSU honor code for Academic Integrity is in effect in this class. It is expected that all students will conduct themselves in an honest manner and NEVER claim work which is not their own. Violating this policy will result in a substantial grade penalty, and may lead to expulsion from the University. You may find it online at http://web.ccsu.edu/academicintegrity/. Please read it carefully.
Students with disabilities: Central Connecticut State University (CCSU) is dedicated to ensuring equal access to academic programs and services in accordance with the Americans with Disabilities Act (ADA) and Section 504 of the Rehabilitation Act. Students with documented disabilities or temporary impairments who require accommodations are encouraged to contact the Office of Accessibility Services (OAS) at 860-832-1952 or via email at accessibilityservices@ccsu.edu. For more information on the registration process for accommodations, please visit the Accessibility Services website at https://www.ccsu.edu/accessibility/. Once accommodations are approved, it is strongly recommended that students discuss their needs with professors at the start of each semester to ensure mutual understanding. Please note that accommodations must be requested each semester and cannot be applied retroactively.
University policies: The university policies are available at https://www.ccsu.edu/sites/default/files/document/SyllabusStatementonDiscriminationandHarassment.pdf. Please read them carrefuly.
Tentative schedule of classes and topics
Note: Links to lecture notes and the dates for classes, assignments, and tests are available in Blackboard.