This course covers fundamental topics in probabilistic modeling and allows you to contribute to this important area of machine learning and apply it to your work. We learn how to model data arising from different fields and devise algorithms to learn the structure underlying these data for the purpose of prediction and decision making. We cover several model classes--including deep generative models--and several inference algorithms, including variational inference and Hamiltonian Monte Carlo.

Machine learning - especially deep learning - is opening new horizons for research in the humanities and social sciences. This course offers a practical introduction to deep learning for graduate students, without assuming calculus/linear algebra or prior experience with coding. By the end of the course, students are able to code a variety of models themselves, including language and image recognition models, and gain an appreciation for the uses of ML in the humanities/social sciences. The course thus aims to support graduate students' professional development and is correspondingly offered in partnership with GradFUTURES.

Introduction to Data Science provides a practical introduction to the burgeoning field of data science. The course introduces students to the essential tools for conducting data-driven research, including the fundamentals of programming techniques and the essentials of statistics. Students will work with real-world datasets from various domains; write computer code to manipulate, explore, and analyze data; use basic techniques from statistics and machine learning to analyze data; learn to draw conclusions using sound statistical reasoning; and produce scientific reports. No prior knowledge of programming or statistics is required.

This course provides the training for students to be independent in modern data analysis. The course emphasizes the rigorous treatment of data and the programming skills and conceptual understanding required for dealing with modern datasets. The course examines data analysis through the lens of statistics and machine learning methods. Students verify their understanding by working with real datasets. The course also covers supporting topics such as experiment design, ethical data use, best practices for statistical and machine learning methods, reproducible research, writing a quantitative research paper, and presenting research results.

The course provides an introduction to modern statistics and data analysis. It addresses the question, "What should I do if these are my data and this is what I want to know"? The course adopts a model based, largely Bayesian, approach. It introduces the computational means and software packages to explore data and infer underlying parameters from them. An emphasis will be put on streamlining model specification and evaluation by leveraging probabilistic programming frameworks. The topics are exemplified by real-world applications drawn from across the sciences.

This course is for graduate students enrolled in the CSML Graduate Certificate Program and is part of the certificate requirements. Students enrolled in the certificate must enroll, attend and present their research during at least one semester. Each week features a presentation by a student, invited faculty or external visitors. All students are required to read materials prior to the workshop and come prepared to engage in conversation. Each week a student presents, a second student introduces the speaker and gives background on the work and a third student moderates the post-presentation discussion.

This is a class about using the tools of machine learning to study social data. The power of machine learning tools is their applicability around a wide range of tasks. There are huge opportunities for applying these tools to learn and make decisions about real people but there are also important challenges. This course aims to (1) show social scientists and digital humanities scholars the potential of machine learning to help them learn about humans, make policy and help people while also (2) showing computer scientists how a social science research design perspective can improve their work and give them new outlets for their skills.