Graph representations of relational data are crucial for data science and machine learning across scientific fields. Graph mining and learning techniques help detect functional modules in biological networks, find communities and missing links in social networks, and perform node-, link-, or graph-level classification. This course equips students with techniques for supervised and unsupervised learning on complex networks. We explore statistical learning methods to infer clusters and predict links, introduce approaches to learn low-dimensional vector representations of graphs, and discuss deep learning applications to complex networks.

This course covers fundamental topics in probabilistic modeling, an important area of machine learning research. We learn how to model data and develop algorithms to learn the structure underlying these data for the purpose of prediction and decision-making. We cover several model classes--conditional and unconditional models--and several inference algorithms, including variational inference, the algorithm behind variational auto-encoders. At the end of the course, students should be well-equipped to come up with a probabilistic model and inference algorithm for their data, and use the fitted model for tasks of interest.

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.

Project-based course in which students work individually/small teams to tackle DS and ML problems, working with real-world datasets.The course emphasizes critical thinking about experiments and dataset analysis and the ability to clearly communicate one's research. Programming components are taught in Python. Experience in only one of the two programming languages (R and Python) is required.This course is intended to support students in developing the analytical skills for quantitative independent work; students should consult with their home department about how this course could complement, not replace, their independent work requirements.

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.