Syllabus
Lecture and section information
INFO 1998, Fall 2018
Lecture time: Wed 5:30pm - 6:30pm
Location location: Gates G01
Staff and office hours
Lecturer: Abby Beeler
- Wednesday: 8:00pm - 9:00pm, Gates G11
Lecturer: Ethan Cohen
- Thursday: 7:00pm - 8:00pm, Rhodes 402
Notice
Please don’t e-mail any of the TAs directly, unless necessary. All questions / queries for help should be done in person during office hours, or on the course Piazza. If there is something urgent going on, we recommend emailing the course manager.
TA: Ann Zhang
- Thursday: 7:00pm - 8:00pm, Rhodes 402
TA: Chris Elliott
- Friday: 2:00pm - 3:00pm, Gates G15
TA: Dylan Tsai
- Wednesday: 8:00pm - 9:00pm, Gates G11
TA: Shubhom Bhattacharya
- Friday: 2:00pm - 3:00pm, Gates G15
TA: Tanmay Bansal
- Friday: 2:00pm - 3:00pm, Gates G15
Catalog description
1 credit. S/U Only. When you finish this program, you will have the foundation and basic skills to contribute to any subteam in Cornell Data Science. This program introduces various machine learning algorithms, model optimization, visualization techniques, and data manipulation strategies, with applications in the Python programming language. The program is open to all Cornell students across all departments.
Prerequisites
One programming course or equivalent programming experience. Preferrably CS 1110 as a pre/co-requisite. No previous knowledge of Machine Learning or expertise in any particular language is assumed.
Course technologies
- We will be working together on in-class assignments/exercises during lectures, so please bring a laptop (or tablet) to fully participate.
- You will need a conda environment and/or virtualenv setup with necessary Python libraries.
- Please refer to the Getting Started page for more information.
Class material
Class material will be posted on our course website, including the assignments, lecture slides, notes, and demos.
We will use CMS for assignment / project submissions and feedbacks.
Course work
In-class assignments
There will be one in-class assignment per lecture, 10 total throughout the semester. All assignments will be done individually. The assignment will be released at the beginning of the lecture (5pm EST on Wednesday), and will be due 5pm EST on Friday through CMS. Each assignment is of reasonable length that you will be able to finish it by end of each lecture, but never force yourself to finish it quickly, and don’t let it disturb you from lecture!
Feedback and Grade Postings
We will be providing you with feedback on the Cornell University Course Management System (CMS). We will grade your work as soon as reasonably possible, latest by Sunday midnight.
Grading
There are three components to grading:
- Attendance: Out of 10 lectures, you should attend at least 7 to pass. Attendance will be accounted through in-class assignment submissions.
- Assignments: Out of 10 in-class assignments, you should submit and get a passing grade on at least 7. Each assignment will be graded based on completion with sufficient amount of effort.
- Project: There will be an open-ended final project near the end of the semester as part of CDS Hackathon. You will need to choose your own competition / research topic and form a group to work together. Project will be graded based on completion with sufficient amount of effort, and the top teams will receive prize.
Course policies
Academic Integrity
All Cornell students are expected to follow the Cornell University Code of Academic Integrity (http://cuinfo.cornell.edu/aic.cfm). Students can consult with the course staffs and other students if they struggle, but all the submissions should be original.