Machine learning (ML) is the scientific study of algorithms and statistical models that computers use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. This course will cover advanced topics in machine learning such as deep learning, transfer learning, multiview learning, clustering and Interpretability of ML methods. Source
Professor: Majid Komeili
Grade: in progress
The course is primarily project-based, with two assignments as well as a major research project. The assignments focused on:
- Convolutional Neural Networks
- Triplet loss
For the research project, I chose to explore large-scale relevance matching using tens of thousands of Tweets and associated news articles. You can check out my work here.
The course also included presentations of two conference papers about a variety of topics. I chose to focus on interpretability in deep learning, and presented Attention is not Explanation as part of a small survey on the interpretability of the attention mechanism.
You can see my slides here (PDF).