Instructors: Prof. Wallach (S2012). Prof. Marlin (F2012, F2013).
Course Description: This undergraduate course is designed to develop the mathematical reasoning skills needed to solve problems involving uncertainty. The skills you will learn are crucial for many exciting areas of computer science that inherently involve uncertainty, including artificial intelligence and machine learning, data mining, financial modeling, natural language processing, bioinformatics, web search and information retrieval, algorithm design, cryptography, system design, network analysis, and more. These skills may also help you analyze the uncertainty in your day-to-day life. The course is divided into three parts: sample space and probability; random variables and expectations; and modeling, inference and estimation.
Instructor: Prof. Marlin (S2011, S2012, S2014).
Course Description: Probabilistic graphical models are an intuitive visual language for describing the structure of joint probability distributions using graphs. They enable the compact representation and manipulation of exponentially large probability distributions, which allows them to efficiently manage the uncertainty and partial observability that commonly occur in real-world problems. As a result, graphical models have become invaluable tools in a wide range of areas from computer vision and sensor networks to natural language processing and computational biology. The aim of this graduate-level course is to develop the knowledge and skills necessary to effectively design, implement and apply these models to solve research problems. This course was previously numbered CMPSCI 691GM.
Instructor: Prof. Marlin (S2013).
Course Description: This seminar will focuses on models and algorithms for supervised and unsupervised machine learning with time series. Topics will include discrete and continuous time models from machine learning, statistics and econometrics. We will investigate a variety of time series problems including prediction, detection, clustering, and similarity search. Coursework for the one credit option includes paper presentations and participation in discussions. Students in the three credit option will also complete a course project. Prerequisites include a graduate-level course in machine learning or graphical models, i.e., CMPSCI 689 or CMPSCI 691GM/688.