Week 1: Course Introduction

Announcements

Friday

Introduction

Welcome to the Machine Learning Course!

This course offers a balanced blend of lectures and hands-on, in-class exercises, designed to deepen your understanding of machine learning concepts. On this page, you’ll find an overview of the tools we’ll use during our labs, along with instructions to help you set up both the CS department’s system and your personal computer.

Your feedback is important to us, and we encourage you to share it throughout the semester. Constructive comments are particularly valuable, as they enable us to make meaningful adjustments to enhance your learning experience.

This semester, we will be using a shared Google folder, in addition to the course website and Blackboard, to manage discussions and announcements. Please ensure you stay engaged with these platforms to keep up with course updates.

Who should take this course?

This course is ideal for computer science students who are passionate about Machine Learning (ML) and have a strong foundation in programming, particularly in Data Structures and Algorithms. While prior experience in ML or research is beneficial, it is NOT required. We welcome students who are eager to explore and learn, regardless of their previous exposure to these areas.

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 your primary resource for staying up-to-date with all course-related information. It is regularly updated with the latest announcements, links to in-class exercises, lab assignments, quiz topics, and other important details such as office hours, TA sessions, and research tips. We recommend bookmarking the website in your preferred browser (Firefox or Chrome) and checking it frequently.

Please note that many aspects of the course, including the schedule, may change throughout the semester. It is your responsibility to review the course website periodically to stay informed of any updates.

Labs

Labs typically begin on Friday and are due by the following Thursday 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

GWU CS Department

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

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