Syllabus
Lecture Information
INFO 1998, Spring 2025
Lecture Time: TBD
Lecture Location: TBD
Staff and Office Hours
Office hours are listed on the home page.
If none of these office hours fit into your schedule, please post your questions to ED and a TA will respond in a timely manner.
Course Description
The goal of this course is to provide you with a high-level exposure to a wide range of Data Science techniques and Machine Learning models, for the purpose of enabling you to solve real problems with machine learning. The course covers getting set up, manipulating and visualizing large datasets, building supervised and unsupervised machine learning models, and a discussion about the various application of these methods in the real world. If you have religiously followed the course throughout the semester, you should expect to have a high-level and intuitive understanding of how data problems could be tackled. You can apply this quick, implementation-oriented toolkit you develop yourself to a variety of fields and problems.
If you are interested in building a solid mathematical foundation for data science and machine learning, this class is not sufficient in and of itself. However, it should serve as a head start for you. We highly encourage you to reach out to course staff if you have any questions about future coursework.
Prerequisites
No prerequisites; Basic Python experience (at the level of CS 1110) is encouraged.
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 CMSx for assignment / project submissions and feedback.
Course Work
Weekly Assignments
One assignment will be assigned at the end of lecture each week, due 11:59pm the next Wednesday. You may skip up to one assignment throughout the semester.
Final Group Project
The final group project is meant to be a culmination of all the knowledge and techniques you acquired during the semester. We will be working on it throughout the course, with a mid-semester check in helping to keep you on track. This is your chance to showcase how much you’ve learned!
Feedback and Grade Postings
We will be providing you with feedback on the Cornell University Course Management System (CMSx). We will grade your work within 8 days of the due date.
Grading
This is a 1-credit S/U class. In order to get a Satisfactory (S) grade, you will need at least 70%.
There are two components to grading:
- Weekly Assignments (55%)
- Group Project (40%)
- Lecture Attendance (5%)
This is a student-run course, so we understand how stressful classes can get. Above all, we want you to enjoy learning and applying the course content. So if you are concerned about passing this class, or have any reasonable cases to make for deadline extensions, please reach out the course manager or post a private note on ED immediately. We would love to see you succeed, but can only help if you notify us in time.
Course Policies
Attendance
Attendance is required and accounts for 5% of your final grade. We also demo code relevant to the assignments, so coming to lecture is in your best interest (we promise)!
Academic Integrity
All Cornell students are expected to follow the Cornell University Code of Academic Integrity. Do not refer to notes from previous semesters or data science projects available online. Our instructors have caught these in the past and the penalty for plagiarism is an unsatisfactory (U) grade.