Johns Hopkins University 3 credits Artificial Intelligence
Machine learning is a subset of artificial Intelligence to build and utilize data models based on sound analytical algorithms. Still, it takes more than just applying a set of algorithms to datasets or experiment a list of toolbox library to successfully build effective machine learning subsystems in an AI system. In this course, we will study a variety of advanced topics involving solutions and novel techniques to various machine learning problems. Starting from Machine Learning Operations, these topics include model analysis such as Recommender Systems, Hyperparameter Optimization, Transfer Learning, and Explainable AI. Moreover, we will study and implement Neural Network machine learning algorithms such as Generative Adversarial Networks, Recurrent Neural Networks, Transformers, and Graph Neural Networks. The course will keep a balance between the theoretical and mathematical specifications of an algorithm and the actual engineering of an algorithm. In addition, we will apply these methods and models, such as GPT, to a variety of real-world problems in realistic course assignments. The course will also keep a research thread with discussions about recent developments, and emerging technologies in the current literature. Students will be expected to write a research paper throughout the course. Prerequisite(s): EN.705.601 OR EN.605.649
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