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.
The deadline to enroll in CIND840 for Winter term is January 14, 2022.
Students will also not be allowed to swap between sections of the Data Analytics courses after January 14, 2022
You must download the Microsoft Remote Desktop in order to access the software needed to complete the requirements for this course. Prior to your first class, you are strongly advised to test the computer you plan to use, 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 should use their own virtual private network (VPN) software to connect to University resources.
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 firstname.lastname@example.org for more information.
A prerequisite may be waived if the student has specific professional experience.
- Practical Data Science and Machine Learning : Required