Week 1: Course Introduction

Announcements

Tuesday

Introduction to CS66

Welcome to CS66!

This course is designed to have a mix of lecture and hands-on, in-class exercises. This page will review some of the tools we will use during the labs and help you get set up with the system in the CS department and your personal computer.

We value all student feedback. Please be constructive in any comments so that we can adjust the course as best possible. This semester, we are using EdSTEM to manage course discussions and announcements.

Who should take this course?

This course is designed for computer science students interested in Machine Learning (ML) and have a solid programming background (CS 35) . Previous ML and experience are nice to have but NOT required. Previous research experience is nice to have but NOT required.

Adding the course, switching sections

If you like to add this course or switch to a different lab section, please contact your course instructor. If you are already on the waitlist, please email your course instructor to sign in at the beginning of each class.

Course Webpage

The Course Website is updated regularly and contains recent course announcements, links to in-class exercises, lab assignments, quiz topics, and other less dynamic course information, including office hours, possible TA sessions, and research tips. Please Bookmark the website in Firefox or Chrome and refer to it regularly. Thanks.

Please be aware that many elements on the course website will change throughout the semester, including the course schedule. It is your responsibility to review the course website periodically for updates.

EdSTEM

This semester, we use EdSTEM to manage course discussions and announcements. If you are registered for the course, you should be automatically enrolled on EdSTEM. If you’re unable to log in or access the course discussion forum, please get in touch with your course instructor — it’s possible something may not be configured correctly for you, especially if you joined after the initial registration phase.

Labs

This course has a mandatory lab section. Attendance is required unless you have completed and submitted the lab for the week before the start of the lab. Labs usually start Wednesday and are due the following Tuesday night.

What is Research?

Three key elements, Innovation, Connection, and Impact.

*Research definition
*Research problems
*Research design
*Research analysis
*Research results
*Research papers
*Other related research topics

Innovation, create or discover something new, an idea, or a method not existing.

Connection, what other researchers have done on this topic? Is there a research gap?

Impact, is the problem important? Is the result significant? Potential to impact how many people? For how long?

Research Paper examples:

Chick the title of each paper. You should see the paper details page.

Please pay attention to the Paper PDF files next to the paper title, and Reviewers' Comments

NeurIPS best papers

Let’s start with these two papers, from the section of Datasets & Benchmarks Best Paper Awards

Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research By Bernard Koch, Emily Denton, Alex Hanna, and Jacob Gates Foster.

ATOM3D: Tasks on Molecules in Three Dimensions By Raphael John Lamarre Townshend, Martin Vögele, Patricia Adriana Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon M. Anderson, Stephan Eismann, Risi Kondor, Russ Altman, and Ron O. Dror.

Here’s a brief introduction about how to review a research paper. Reviewer Guide

Please read the following two sections for this week: Review content, and Examples of Review Content.

What is CS Research?

Let’s take a look at examples: Top CS Publications

Swarthmore CS

Our Research

In-class exercises:

*Explain to your classmates the popular topics in these CS research publications.
*Explain to your classmates what topic(s) you may be interested in and why.

What is Machine Learning (ML)?

What’s the relationship between Machine Learning, Deep Learning, and Artificial Intelligence? Let’s take a look at examples: Top AI Publications

NeurIPS, our ten-page paper, final project is based on this.

AAAI, our two-page poster, mid-term project is based on this.

AAAI accepted posters

In-class exercises:

*Explain to your classmates what is Machine Learning
*Explain to your classmates the popular topics in AAAI 21 student posters.

More Machine Learning Resources

Thursday

New in Machine Learning

Please review the videos:

Background about this video and the slides: New in Machine Learning Research Video Link: Video Link, please click

Invited Talk: Sinead Williamson (UT Austin, ICML Newcomers Chair)

start from 4 hours 05 minute, end at 4 hours 40 minute of this video

Invited Talk: Nicolas Le Roux (Google Brain Montreal)

start from 3 hours 20 minute, end at 4 hours 05 minute of this video

In-class exercises:
*Explain to your classmates what's in these two videos and your understanding of research.
*Explain to your classmates which part of the video you find most helpful and why.
*Example: start from 4 hours 25 minutes, end at 4 hours 40 minutes, 'how to find research mentors'.

New in ML, more sessions

ICML ICML 2022, ICML 2020

NeurIPS NeurIPS 2021, NeurIPS 2020, and NeurIPS 2019