This highly-applied course surveys a variety of key of concepts and tools that are useful for designing and building applications that process data signals of information. The course introduces modern open source, computer programming tools, libraries, and code samples that can be used to implement data applications. The mathematical concepts highlighted in this course include filtering, prediction, classification, decision-making, Markov chains, LTI systems, spectral analysis, and frameworks for learning from data. Each math concept is linked to implementation using Python using libraries for math array functions (NumPy), manipulation of tables (Pandas), long term storage (SQL, JSON, CSV files), natural language (NLTK), and ML frameworks.

This course continues the discussion of key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, sports, the Internet, and more. Students will gain experiences working with various analytics packages in Python, and deliver a comprehensive team project. Topics include: deep learning, reinforcement learning, end-to-end learning, casual interference, and ethics, fairness and safety in artificial intelligence. students will gain experience understanding and applying these techniques.

Repeat Rules

Course is not repeatable for credit.