SIPB IAP 2022 Activities

What is this?

SIPB, the computing club at MIT, sponsors a series of classes over IAP. Visit our homepage.


Calendar Version

These events are available as a Google Calendar along with the SIPB calendar.


What else happens during IAP?

See the official IAP activities index.

I have a question about {x}

Contact sipb-iap at mit dot edu.

Practical Machine Learning

Crystal Su, Ashay Athalye
Date:
  • Tue Jan 18 12:00 PM – 2:00 PM in 32-155
  • Wed Jan 19 12:00 PM – 2:00 PM in 32-155
  • Thu Jan 20 12:00 PM – 2:00 PM in 32-155
  • Fri Jan 21 12:00 PM – 2:00 PM in 32-155
  • Mon Jan 24 12:00 PM – 2:00 PM in 32-155
  • Tue Jan 25 12:00 PM – 2:00 PM in 32-155
  • Wed Jan 26 12:00 PM – 2:00 PM in 32-124***
  • Thu Jan 27 12:00 PM – 2:00 PM in 32-155

This is a fast-paced course that teaches machine learning techniques through practical code examples and exercises. This class is intended for students with some experience/background in programming who want to learn how to quickly apply machine learning methods to solve problems, without necessarily needing to understand the math behind them. It is not meant as a replacement for an actual machine learning class.

Topics include regression, nearest neighbors, decision trees, random forests, recurrent neural networks, convolutional neural networks, and (possibly) generative adversarial networks.

Attendance: Everyone is welcome!
Contact: Crystal Su, cbsu at mit dot edu

Command Line Fundamentals

Anthony Grebe, Huy Dai, Javier Solis
Date:
  • Thu Jan 20 3:00 PM – 4:30 PM in 2-131
  • Fri Jan 21 3:00 PM – 4:30 PM in 2-131

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 2-course series 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.

Jan 20: The Basics
Jan 21: Advanced Features

TL;DR: Intro version of The Missing Semester

Attendance: Everyone is welcome!
Contact: Javier Solis, javsolis at mit dot edu

Scientific Computing in Python: A NumPy Crash-Course Cluedump

Mark Chilenski
Date:
  • Tue Jan 25 7:00 PM – 8:00 PM in 1-190

NumPy is the standard tool for manipulating arrays of numeric data in Python, and has heavily influenced the design and implementation of many other libraries for scientific computing and machine learning. This talk will be a self-contained "crash course," intended to get someone with little or no NumPy experience to the point that they can confidently start manipulating arrays. The talk will emphasize principles such as broadcasting and vectorization which will also help intermediate NumPy users to write cleaner, more efficient code.

Attendance: Everyone is welcome!
Contact: Mark Chilenski, markchil at mit dot edu