An introduction to AI with a strong emphasis on machine learning (ML). The successful student will: apply modern AI frameworks like Keras and Scikit Learn, and will be able to describe and use tools like Jupyter notebooks and/or cloud computing for training AIs; attack learning problems using decision trees & forests for expert systems, deep, convolutional and/or feedback artificial neural networks for supervised learning (including natural language), clustering and/or other unsupervised learning techniques; Apply search techniques (tree and gradient) to reasoning and/or optimization problems.; Reflect on learning from data as an IT, IP, and ethical problem, among other ethical and cultural issues around AI; Demonstrate a better-than-popular-press understanding of Large Language Models, including word embeddings. Prerequisite: CS 1113 and MA 134