Machine Learning and Data Analytics II

4 credits · Undergraduate

Following IEOR 142A/242A, this course further introduces students to essential methodologies and recent trends in machine learning and data analytics. The course will bridge theoretical foundations with applied data analytics by using examples and real datasets from domains such as e-commerce, social media, finance, and more. Students will gain experience with various data analytics packages in Python and will deliver a comprehensive team project. Topics include: deep learning, time series and survival analysis, end-to-end learning, causal inference, reinforcement learning, and ethics, fairness and safety in artificial intelligence.