This course will introduce the foundations of modern artificial intelligence (AI) and key ideas and techniques underlying the design of intelligent computer systems. It will focus on concepts that are not only important in the space of AI but are also practically useful in modern applications. The general topics of the course will include a variety of knowledge representations and algorithms for interference, decision-making, planning, and learning.

This course covers manipulating structured data using different data management techniques, and analyze data requirements. Students learn to design relational databases and use SQL to define, query and update them and explore non-relational schemaless databases, and query them. Concepts of the cloud, big data, and cybersecurity as they relate to the management of database systems.

This course explores how to combine the complementary strengths of humans and AI to design intelligent interactive systems that are ethical, usable, and useful. Topics can include AI/human computation, plan and activity recognition, smart sensing/homes, active learning, preference elicitation, intelligent / adaptive user interfaces, and mixed human-agent simulations. Students learn how to design and develop intelligent interaction technologies while also critically assessing their social and ethical impact.

This course focuses on advanced machine learning topics such as learning theory, transfer learning, meta-learning, domain adaptation, self-adaptive learning algorithms, deep learning, Gaussian processes, and kernel methods. This course will involve the study of a variety of machine learning techniques for predictive analytics. Particular emphasis will be given to approaches that are scalable to very large data sets and/or those that are relatively robust when faced with a large number of predictors, and algorithms for heterogeneous or streaming data.

This course deals with the analysis of algorithms and the relevance of such analysis to the design of efficient algorithms. Topics include asymptotic analysis, average-case and worst-case analysis, recurrence analysis, amortized analysis, classical algorithms, computational complexity analysis, NP-completeness, and approximation algorithms. In addition, the course investigates approaches to algorithm design including greedy algorithms, divide and conquer, dynamic programming, randomization, and branch and bound.

This course deals with the analysis of algorithms and the relevance of such analysis to the design of efficient algorithms. Topics include asymptotic analysis, average-case and worst-case analysis, recurrence analysis, amortized analysis, classical algorithms, computational complexity analysis, NP-completeness, and approximation algorithms. In addition, the course investigates approaches to algorithm design including greedy algorithms, divide and conquer, dynamic programming, randomization, and branch and bound.

This course deals with the analysis of algorithms and the relevance of such analysis to the design of efficient algorithms. Topics include asymptotic analysis, average-case and worst-case analysis, recurrence analysis, amortized analysis, classical algorithms, computational complexity analysis, NP-completeness, and approximation algorithms. In addition, the course investigates approaches to algorithm design including greedy algorithms, divide and conquer, dynamic programming, randomization, and branch and bound.

This course deals with the analysis of algorithms and the relevance of such analysis to the design of efficient algorithms. Topics include asymptotic analysis, average-case and worst-case analysis, recurrence analysis, amortized analysis, classical algorithms, computational complexity analysis, NP-completeness, and approximation algorithms. In addition, the course investigates approaches to algorithm design including greedy algorithms, divide and conquer, dynamic programming, randomization, and branch and bound.