/AI-Penn

A repository to store all the source code and learning resources for AI@Penn's technical bootcamps.

Primary LanguageJupyter Notebook

AI@Penn Code + Resources Repository

A repository to store all the source code and learning resources for AI@Penn's technical bootcamps.

Session 1 (KJ + Aidan)

  • Brief intro to Machine Learning.
  • Overview of SVMs, K-Nearest Neighbours algorithm.
  • Basics of numpy and pandas.
  • Programming Session: Social media advertisment success prediction (classification).

Session 2 (Michael + Wyatt)

  • Overview of Logistic Regression.
  • Overview of Decision Tree and Random Forest algorithms.
  • Introduce and apply data visualization concepts.
  • Programming Session: Credit default prediction (classification).

Session 3 (Joanna + Larry + Pranav)

  • Overview of Linear Regression.
  • Introduce and apply data preprocessing techniques.
  • Programming Session: Real estate valuation (regression).

Session 4 (Keshav + Emily)

  • Overview of standard Neural Networks.
  • Introduce data normalization concepts (like Dropout).
  • Programming Session: Predicting outreach/shares of news articles (regression).

Session 5 (Michael + Wyatt)

  • Overview of Recommender Systems.
  • Programming Session: Predicting movie recommendations based on reviews.

Session 6

  • Overview of Long-Short Term Networks (LSTMs).
  • Introduce time-series data.
  • Introduce using APIs to get live data.
  • Programming Session: Predicting stock prices using historical data (time series).

Session 7

  • Overview of Convolutional Neural Networks (LSTMs).
  • Introduce web scraping.
  • Programming Session: Differentiating between two types of objects, eg. Football vs Basketball - comparison choice is up to students (computer vision).