This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve both the traditional and the novel data science problems found in practice. This course will also serve as a foundation on which more specialized courses and further independent study can build.
This is a required course for the Center for Data Science's Masters degree in Data Science, and the course is designed for the students in this program. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that this class is a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this class (e.g. data cleaning, cross-validation, decision trees, and sampling bias).
Course details can be found in the syllabus.
This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the Lab Instructor, graders, and the Instructor. Rather than emailing questions to the teaching staff, you are encouraged to post your questions on Piazza. If you have any problems or feedback for the developers, email firstname.lastname@example.org. Without registering, you can also view an anonymized version of our Piazza board.
See the Course Calendar for all schedule information.
For registration information, please contact Varsha Tiger.