Exam and Assignments
This course consists of a written examination, two lab assignments, and one final project. The labs and project should be done in groups of two and have to be submitted before their deadlines. The examination and the labs are graded A-F, while the project is graded P/F, and students should submit them before the deadlines. A late submission will reduce the assignment’s grade level by one. That is, A will become B, B will become C, and so on. When delivering the labs and the final project, in addition to the source code, students should give an oral presentation of their code and answer questions that include basic and advanced questions. The details of the grading are presented in the slides of the introduction lecture.
Exam
The exam will be based on the material from the lectures and the referenced material in the coursebooks.
Lab Assignments
The lab assignments span over different topics of the course. For each lab, a zip file is given that includes instructions for doing the assignment.
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Lab 1: Instructions
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Lab 2: Instructions
Project
You should define your own project by writing at most one page description of the project. The proposed project should be approved by the examiner. The project proposal should cover the following headings:
- Problem description: what are the data sources and the prediction problem that you will be building a ML System for?
- Tools: what tools you are going to use? In the course we mainly used Decision Trees and PyTorch/Tensorflow, but you are free to explore new tools and technologies.
- Data: what data will you use and how are you going to collect it?
- Methodology and algorithm: what method(s) or algorithm(s) are you proposing?
What to deliver
You should deliver your project as a stand alone serverless ML system. You should submit a URL for your service, a zip file containing your code, and a short report (two to three pages) about what you have done, the dataset, your method, your results, and how to run the code. I encourage you to have the README.md for your project in your Github report as the report for your project.