Lectures

Lecture 1 - Introduction [Page] [Slides] [Video]

Lecture 2 - Social Good Domains [Page] [Slides] [Video]

Lecture 3 - ML basics [Page] [Slides] [Video]

Lecture 4 - ML pipeline with sample data [Page] [Slides] [Video]

Lecture 5 - ML pipeline (contd.), Neural networks [Page] [Slides] [Video]

Lecture 6 - Neural networks, RNN, LSTM [Page] [Slides] [Video]

Lecture 7 - Social good applications, assignment [Page] [Slides] [Video]

Lecture 8 - Assignment, data analysis [Page] [Slides] [Video]

Lecture 9 - Data analysis, Projects, GHTC [Page] [Slides] [Video]

Lecture 10 - Projects, GHTC, Metrics [Page] [Slides] [Video]

Lecture 11 - Projects, GHTC, Metrics and Evaluation [Page] [Slides] [Video]

Lecture 12 - Embeddings, Transfer Learning [Page] [Slides] [Video]

Lecture 13 & 14 cancelled.

Lecture 15 - Schedule, Social good application design [Page] [Slides] [Video]

Lecture 16 - Deployment methods [Page] [Slides] [Video]

Lecture 17 - Recommender Systems [Page] [Slides] [Video]

Lecture 18 - Recommender Systems, Time Series, IR, KD [Page] [Slides] [Video]