A repository to store all the source code and learning resources for AI@Penn's technical bootcamps.
- 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).
- Overview of Logistic Regression.
- Overview of Decision Tree and Random Forest algorithms.
- Introduce and apply data visualization concepts.
- Programming Session: Credit default prediction (classification).
- Overview of Linear Regression.
- Introduce and apply data preprocessing techniques.
- Programming Session: Real estate valuation (regression).
- Overview of standard Neural Networks.
- Introduce data normalization concepts (like Dropout).
- Programming Session: Predicting outreach/shares of news articles (regression).
- Overview of Recommender Systems.
- Programming Session: Predicting movie recommendations based on reviews.
- 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).
- 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).