DS 340 covers the most important concepts and algorithms in AI and machine learning, ranging from search to deep neural networks, with an eye toward conceptual understanding and building a final project. Important topics include varieties of search (for lookahead), probabilistic reasoning, gradient descent applied to neural networks, applying regularization, reinforcement learning, the role of embeddings in natural language processing, and the role of attention in transformer architectures (eg, BERT and GPT4). Applications include image classification, sentiment analysis, game playing, and recommender systems, as well as a cursory introduction to generative AI. A background in Python programming is necessary, while multivariable calculus, linear algebra, and probability allow a deeper understanding of the material. Effective Fall 2022, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Quantitative Reasoning II, Critical Thinking.

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