|Introduction to Machine Learning||Slides||Slides with Answers|
|Course Information||Slides||Annotated Slides|
|Linear Algebra (Handout)||Slides|
|Week 1-6 (Linear Regression, SGD, Least-Squares, Ridge Regression, Cross-Validation, Bias-Variance decomposition)||Notes||Annotated|
|Week 7-8 (Classification, Logistic Regression, k-NN, DNNs)||Notes|
Every (registered) student is supposed to complete a project, either alone or with another student in a team of two. Grading will be based on the constructive feedback from the class on the project report and presentation.
The main goal of the project is for students to get hands-on experiences on some of the concepts taught in the course. Students are advised to pick a project in consultation with the teaching team. Our hope is that the project will not only teach you but also give you a taste of how it is like to work on ML research projects, and how the submission and review process on conferences like NeurIPS/ICML/ICLR works.
One strategy could be to first think about a concept and topic that you might want to learn about, and choose a project that can be realistically finished in 2 months time. Our recommendation is to not choose too ambitious project, rather some simple and realistic. Remember that it takes (almost always) much more time than what we often anticipate. So one should leave enough room for uncertainties that may arise during the execution of the project.
The 4-page final project report (with supplementary and code) is due on Aug 12, 5pm JST.
To avoid last-minute rush to the deadline, we recommend students to follow the following rough schedule,