In the assignments (and lab exercises), you will implement (and experiment) a few widely used machine learning algorithms.

• There are 2 mandatory assignments and 1 project (with presentation) in this course. Each of them will be released/accessible after the related lectures have been delivered.
• Groups of three (ideally) students will work on assignments/project together. It is essential that every group member plays an active role, including in writing codes AND in writing the report. A division of tasks based on handing the role of report writing to one person and code writing to another is strongly discouraged!
• Your report needs to include a brief description of how the tasks were distributed among the team members. This is used to differentiate individual grades (see here for an example of the report). Note that this is not a template for reports and feel free to use your own format (LaTex is recommended).
• Flexible & strict deadlines: For the assignments, max 3-day delay allowed (no deduction), or max 55% granted after the 3-day extension. The project has a strict deadline.

Read the instruction carefully before you start!

The assignment materials are available on BrightSpace under the "Assignments" tab. After you complete an assignment, submit it to BrightSpace (for the university to have a permanent record of your work). You will also receive the mark for each assignment on BrightSpace.

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