Machine learning 2

I assume you have taken ML1 (ds4400) or equivalent. Working knowledge of Python required (or you must be willing to pick up rapidly as we go).

Homeworks will consist of both written and programming components. The latter will be completed in Python, using a mix of standard libraries (numpy, pytorch, etc.)

Late Policy. Homeworks that are one day late will be subject to a 20% penalty; two days incurs 50%. Homeworks more than two days late will not be accepted.

The mid-term will be given in class, and will be testing for understanding of the core material presented in the course regarding the fundamentals covered in the first half of the course.

A big component of this course will be your project, which will involve picking a particular dataset on which to implement, train and evaluate machine learning models. Collaboration is allowed (team sizes <= 2, however). This project will be broken down into several graded deliverables, and culminate in a report and final presentation in class to your peers.

Here is an outline of the project expectations, (tentative) dates, etc.

A commitment to the principles of academic integrity is essential to the mission of Northeastern University. The promotion of independent and original scholarship ensures that students derive the most from their educational experience and their pursuit of knowledge. Academic dishonesty violates the most fundamental values of an intellectual community and undermines the achievements of the entire University. For more information, please refer to the Academic Integrity Web page.

More specific to this class: It is fine to consult online resources for programming assignments (of course), but lifting a solution/implementation in its entirety is completely inappropriate. Moreover, you must list all sources (websites/URLs) consulted for every homework; failing to do so will constitute a violation of academic integrity. In general, you must also be able to explain whatever code you use.