Courses

Machine Learning Engineering (Cornell Tech, F 2020) Machine learning systems are increasingly being deployed in production environments, from cloud servers to mobile devices. This course will focus on challenges inherent to engineering machine learning systems to be correct, robust, and fast. Assignments will be project focused, with students building and deploying systems for applications such as text analysis and recommendation systems. In addition to machine learning models, practical topics will include: tensor languages and auto-differentiation; model debugging, testing, and visualization; compression and low-power inference. Guest lectures will cover current topics from local ML engineers.

Elements of Data Science (Harvard, S 2021) Data science combines data, statistical analysis, and computation to gain insights and make useful inferences and predictions. This course will take a holistic approach to helping students understand the key factors involved, from data collection and exploratory data analysis to modeling, evaluation, and communication of results. Working on case studies and a final project in teams will provide students with hands-on experience with the data science process using state-of-the-art tools. Emphasis will be given to the strengths, trade-offs, and limitations of each method to highlight the importance of merging analytical skills with critical quantitative thinking.