Neural nets, encryption, and more

Today’s meeting was jam packed with everything from neural networks to work in progress on an automated way of doing homework.

Neural Networks: Samson introduced us to neural networks, and how to learn weights of a neural network by backpropogating the errors and using gradient descent. [Separate post pending.]

Homomorphic Encryption: Diran talked about homomorphic encryption, an encryption schema where one can perform operations (such as addition) on the encrypted messages and get meaningful results. With an encryption schema that preserves addition and multiplications, we can do things like linear regression on encrypted data. [Separate post pending.]

AOL Search Dataset: Jee explained the AOL search dataset, a dataset consisting of an anonymized list of users and the terms they searched for on AOL over the span of three months. Previous projects that use this dataset include the query “topic” connection visualization  (type something on the gray area at the top of the page) and a visualization of question queries.

Jimmy also presented on a work in progress: an automatic homework solution generator that outputs latex code for solving a given formula.

Next meeting will be next Tuesday Nov 8th at 3pm in the Stats Club Office M3 3109.