Fall 2019 Machine Learning Courses
Instructor: Diyi Yang
This seminar is about using a variety of techniques from natural language processing, machine learning, and social science to understand human language. Possible topics include analysis of language influences, biases, language civility, discourse and conversations.
Instructor: Swati Gupta
At the heart of most machine learning applications today – like advertisement placement, movie recommendation, and node prediction in evolving networks – is an optimization engine trying to provide the best decision with the information observed thus far in time, i.e. the problem of online learning. One must make online, real-time decisions and continuously improve the performance with the arrival of data and feedback from previous decisions. The course aims to provide a foundation for the development of such online methods and for their analysis. At times when these decisions are viewed in the context of the background of the consumers, there is some perceived bias or discrimination. The course will also discuss ethical and legal issues that arise due to such unintended consequences of decision-making and mathematical tools to formalize as well as mitigate such effects.
CS 3600: Introduction to Artificial Intelligence
Instructor: Mark Riedl
Introduction to the broad field of AI and ML.
Instructor: Dhruv Batra
Instructor: Jacob Abernethy
Advanced algorithms course, with a focus on topics in optimization and data engineering.
Instructor: Ghassan AlRegib
Image Processing, Learning and Transforms
Instructor: Siva Theja Maguluri
Introductory Probability Theory, Markov Chains, Poisson Processes, Renewal Theory
Instructor: David Byrd
An introduction to computational investing, applied machine learning, and the combination of the two, with projects in python3 + numpy + pandas + matplotlib.
Instructor: Thomas Ploetz
Grad level overview of methods of Artificial Intelligence
Instructor: Justin Romberg
The basics: linear algebra and least-squares, bases and kernel spaces, basic statistical inference and learning theory
Instructor: Alexander Lerch
This course covers the basic approaches for musical content analysis and teaches students to approach this class of problems and think algorithmically. Topics include pitch tracking, beat tracking, audio feature extraction, and genre classification.
Instructor: Polo Chau
Data science course that introduces students to broad classes of techniques and tools for analyzing and visualizing data at scale.
Instructor: Constantine Dovrolis
It is often the case that complex systems, both living and man-made, can be represented as static or dynamic networks of many interacting components. These components are typically much simpler in terms of behavior or function than the overall system, implying that the additional complexity of the latter is an emergent network property.
Network science is a relatively new discipline that investigates the topology and dynamics of such complex networks, aiming to better understand the behavior, function and properties of the underlying systems.
The applications of network science cover physical, informational, biological, cognitive, and social systems. In this course, we will study algorithmic, computational, and statistical methods of network science, as well as applications in communications, biology, ecology, brain science, sociology, and economics. The course will go beyond the strictly structural concepts of small-world and scale-free networks, focusing on dynamic network processes such as epidemics, synchronization, or adaptive network formation.
Instructor: Chao Zhang
This course introduces state-of-the-art machine learning techniques for mainstay problems in text data analysis, with particular emphasis on deep learning methods that have recently achieved enormous success.
CS7641: Machine Learning
Instructor: Charles Isbell
Graduate introduction to machine learning
CS7642: Reinforcement Learning
Instructor: Charles Isbell
Graduate Introduction to Reinforcement Learning and Decision Making
Instructor: Rachel Cummings
How should we define privacy? How can we enable the analysis of data containing sensitive information about individuals while protecting the privacy of those individuals? What are the tradeoffs between useful analyses of large datasets, and the privacy of the individuals from whom the data are derived? This course will take a mathematically rigorous approach to addressing these and other questions at the foundations of research in data privacy. This course will take a mathematically rigorous approach to addressing these and other questions at the foundations of research in data privacy. We will draw connections to a wide variety of topics, including economics, statistics, optimization, learning theory, information theory, and approximation algorithms.
Instructor: Joel Sokol
Gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools.
Instructor: Irfan Essa
Instructor: Irfan Essa
MGT 6509: Legal and Ethical Considerations in Business
Instructor: Deven Desai
This course looks at legal and ethical considerations in business. Materials will cover issues related to technology and business and likely include issues around using of ML for credit scoring, employment, and other areas of application. The course is case-based and involves consideration of simpler technology too such safety devices and design of product issues.