SIPB IAP 2023 Activities
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Calendar Version
What else happens during IAP?
See the official IAP activities index.
I have a question about {x}Contact sipb-iap at mit dot edu.
[Cluedump] Adapting to Modern Tools: Implications of Large Language Models |
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Madison Landry Date:
Join us for a one-hour workshop on the implications of recent advancements in natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) on computation, computer programming, and beyond. We will discuss the moral questions raised by the deployment of these technologies and investigate how they may be utilized fairly & wisely in the classroom. Students who are interested in learning more about how to make the most of these resources at MIT for learning rather than cheating are encouraged to attend this program. Expect a mix of presentations and hands-on activities, as well as opportunities to engage with the material and ask questions. Prerequisite(s): None |
Command Line Fundamentals |
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Anthony Grebe, Javier Solis Date:
Do you find yourself needing to use the command line for your class, research project, or internship, but don't know how to use it properly? Would you like to be more comfortable using text-based interfaces? In this crash-course hosted by SIPB, MIT's computer club, we will walk you through the fundamentals of working in the terminal (how to ssh, navigate directories, edit files, etc.), along with more advanced features, such as managing packages, piping command outputs, copying files from remote servers, and more! Mastering this tool will in the long run save countless hours and facilitate your workflow. Prerequisite(s): None |
Designing Good Programs With Types |
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CJ Quines, Jason Chen Dates:
Learn how types can help you be a better programmer! We'll learn some functional programming, and some type theory principles that apply no matter what language you're using. Learn about how type safety can give us better mental models, what algebraic datatypes or dataclasses are, the importance of pure functions and testing, the meaning of "parse, don't validate", and type-safe error handling. There'll be exercises and projects where you get to apply what we're learning. Prerequisite(s): Python, on the level of 6.1010 [6.009]. Bring a laptop with Python 3.10 or higher. (Older versions won't work.) |
[Cross-over] Introduction to Data-Centric AI |
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Anish Athalye, Curtis Northcutt, Jonas Mueller, Cody Coleman, Ola Zytek, Sharon Zhou Dates:
Typical machine learning classes teach techniques to produce effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. This class provides an introduction to the emerging science of Data-Centric AI (DCAI) that studies techniques to train better ML models by improving the data. Learn more at dcai.csail.mit.edu. Prerequisite(s): None; 6.036 or similar recommended. |