The goal of this course is to provide you with the necessary technical expertise to critically reflect on the problems that AI systems are meant to be solving, to identify the strengths and weaknesses of existing AI systems, and to build impactful AI systems yourself. Specifically, the course covers three broad topics: (1) Linear and shallow models to develop enough philosophy and critical thinking about practical problem formulation skills, complex modeling objectives and computational frameworks; (2) Expanding these to deep and non-linear models such as gradient boosted trees, convolutional neural networks, and transformers; (3) Evaluating the applicability and ethics of these models on a range of applied problems. All three topics will be heavily relying on writing code, as well as critically reviewing various literature sources from industry and academia.
The course heavily focuses on using Python and Github. Foundational knowledge in statistics, mathematical analysis, and Python programming and Version Control is assumed.