CIND 840 - Practical Approaches in Machine Learning
Course DescriptionThis course covers the major ideas and principles underlying current practices of data mining. It starts with what machine learning is, where it can be used and different kinds of knowledge representations that are involved. Then, the course spans advanced techniques of data mining, at the lowest and most detailed levels. Finally, the course is wrapped up by covering techniques of "ensemble learning", which combine the output from different learning techniques. On each topic, examples in Python are provided as lab materials.
Students are recommended to check Important Dates for the Chang School current term before enrolling in the course and paying the fees. Notably, the Azure Virtual Desktop assigned to students will be accessible two days after the course’s starting date, and swapping between sections will not be permitted.
Students are also encouraged to download the Microsoft Remote Desktop app to access the software needed to complete this course’s requirements. Students also need to test the compatibility of the computer they plan to use before the first session, as machines operated using a third-party administrator, such as laptops provided by a workplace, may not allow access to the required software/download(s). International students might need to use their virtual private network (VPN) software if they cannot connect to University resources.
Students who wish to take CIND 840 and CIND 719 Big Data Analytics Tools concurrently, as part of the Practical Data Science and Machine Learning Certificate should contact Ceni Babaoglu, Assistant Program Director, Data Science at firstname.lastname@example.org.
Prerequisites: CMTH 642
Registered certificate program students who do not have the prerequisite and who wish to take this course should submit this form: Request Department Consent, or contact Ceni Babaoglu, Assistant Program Director, Data Science at email@example.com for more information.
A prerequisite may be waived if the student has specific professional experience.
- Practical Data Science and Machine Learning : Required