(CS 101) Projects in Machine Learning


2017 Fall Term

Course Description

Prerequisite: CS 155 or equivalent

This is a project-based course for students looking to gain practical experience in machine learning. Students are expected to be proficient in basic machine learning. Students will work in groups. Each group will be provided a project topic to work on along with domain expert advisors. Alternatively, students can propose their own projects, subject to approval by course instructors.

Course Details

Instructor

Yisong Yue               yyue@caltech.edu
Omer Tamuz            omertamuz@gmail.com

Teaching Assistants

tba

List of Projects

subject to change


Bridge AI. Building a team of collaborating AI to learn to play bridge.


Shakespeare Neural Translator. Train a neural net to translate between normal English and "Shakespeare" English.


Script generation. Train a neural net to translate between normal English and "Shakespeare" English.


Learning an Optimizer. Train a neural net to solve hard combinatorial optimization problems, such as Traveling Salesman.


Computational humor. Write a program that makes people laugh (with it, not at it). Some options include:


Adaptive Teaching. Teach children by learning on the fly what they know and don't know and choosing learning tasks accordingly.


Minimum Intelligent Signal Test. This test is a variant of the Turing test, in which the a tester asks a responder questions, and the responder is only allowed to answer "yes" or "no".


Learning to Run. Train a controller to run in a simulated bipedal robot


Learning to Play Atari from Demonstration. Train an AI agent to play Atari by learning from human demonstrations.