| 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 | Annotated |
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,
You must detail your analysis in a report. You should include complete details of what you did. You should clearly state your conclusions. You should argue that the results you get make sense (or do not make sense), and what could be the reason behind it.
Your report should not be longer than 4 pages!
You must use the latex style given below (in the latex source). We have also given you a sample report that shows the format of this style file. The report details the demo done during an exercise session. Do not copy the content and figures or even the analysis of this report. This report is for illustration purpose only!
Sample Report (from an older course)
Bonus: here is a sample marked report
You can learn Latex using tutorials given below. We will help if you ask us.
You can read the following paper on how to write a machine learning paper. Section 2 and 4 are highly relevant.
http://www.maths.tcd.ie/~dwilkins/LaTeXPrimer/ - tutorial on Latex
http://www.stdout.org/~winston/latex/latexsheet-a4.pdf - cheat sheet with useful commands for Latex
http://mirror.switch.ch/ftp/mirror/tex/info/first-latex-doc/first-latex-doc.pdf - example how to create a document with Latex
http://en.wikibooks.org/wiki/LaTeX - detailed tutorial on Latex
Here are a few things to check to make sure your report is good enough. Read the following carefully.