Office: Loop Campus, CDM Building, Room 833
Phone: (312) 362-5174
Office Hours: Mondays 4:00-5:30 PM (or by appointment)
The course will focus on the implementations of various data mining and machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. Additional open source machine learning and data mining tools may also be used as part of the class material and assignments. Students will develop hands on experience developing supervised and unsupervised machine learning algorithms and will learn how to employ these techniques in the context of popular applications such as automatic classification, recommender systems, searching and ranking, text mining, group and community discovery, and social media analytics.
CSC 401 and IS 467 (formerly IS 567)
TEXTBOOKS & COURSE MATERIAL
We will use numerous online resources and documents throughout the course. The required and recommended textbooks are listed below. The resources directly relevant to topics covered in the course are listed in the Course Material section. Additional resources can be found on the Resources section.
GRADING & COURSE REQUIREMENTS
The structure and grading in the class will be centered around 4-5 assignments and a final project. The assignments will involve Python implementations of selected data mining techniques and their applications in various domains. The assignments will typically involve both programming components as well as problems related to the material covered in class. Some assignments may also involve the use of other open source data mining tools. These assignments must be done individually, unless otherwise specified. Late assignments will be penalized 10% per day (with weekends counting as one day).
The final project will be a more complex programming/implementation assignment that will involve integrating multiple concepts and techniques. Student will be able to choose from among several possible projects ideas or propose their own. More details on the final project are available in the Project section.
The final grade will be determined (tentatively) based on the following components:
Final Project = 35%
TENTATIVE LIST OF TOPICS
The following issues and topics will be covered throughout the course. Many of these topics will be revisited several times during the course in a variety of contexts.
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