This course is leveraging materials from the Kaggle platform and utilizes Kaggle notebooks. It represents an elective component of the Master's program in Advanced Data Science (IASD M2) offered at the institution. The curriculum is designed to leverage the comprehensive tools and datasets available on Kaggle to provide a practical, hands-on learning experience in data science.

This course will assume some familiarity with machine learning concepts, proficiency in Python programming, and familiarity with statistical methods. Participants are expected to have foundational knowledge equivalent to what is taught in introductory machine learning courses. For those seeking to build or refresh their understanding of fundamental machine learning principles, it is recommended to explore the course offered by Berkeley, CS189, which provides a thorough introduction to the field. More details about this preparatory course can be found on the Berkeley CS189 class

There will be five homeworks. For each homework, we will post a PDF on the front page and starter code that will be posted on Github.

We will post slides on the front page after each lecture.

All homeworks should be done individually.

For the final project, you may work in groups of up to three people. Each group will submit a report. The expectations for the project scope will increase depending on the number of students in each group, and for groups of two or three, we will also expect a short paragraph to explain the role of each group member along with the final report. From past experience, groups of two tend to be the most effective, though you may work in a group of three or alone. Groups larger than three are not permitted without special permission from the course staff.

All assignments must be turned in via Moodle on time. We will allow a total of five late days cumulatively. We will not make any additional allowances for late assignments: the late days are intended to provide for exceptional circumstances, and students should avoid using them unless absolutely necessary. Any assignments that are submitted late (with insufficient late days remaining) will not be graded.

Late days may not be used for quizzes, final project proposals, final project milestone reports, final project reports, or any of the project peer review reports, only for the five homeworks.

• Homework: 50% (10% per HW x 5 HWs)
• Final Project: 50%