Campus: OU Polytechnic
Summary: This 60-hour bachelor degree completion program is designed to be completed in two years and developed with the needs of local, state, and regional employers in mind. Upon graduation, students will hold a Bachelor of Science in Applied Artificial Intelligence from the University of Oklahoma. Your OU degree in Applied Artificial Intelligence will make you uniquely prepared to take advantage of many career opportunities. Combining hands-on learning with projects that span multiple relevant fields of study, you will make real-world connections through internships and industry-sponsored senior design projects.
Courses in the curriculum will include:
Instructor: Chao Lan
Summary: Study of the methods of search, knowledge representation, heuristics, and other aspects of automating the solution of problems requiring intelligence.
Instructor: Chao Lan
Summary: Topics include decision trees, relational learning, neural networks, Bayesian learning, reinforcement learning, multiple-instance learning, feature selection, learning appropriate representations, clustering, and kernel methods.
Instructor: Andrew Fagg
Summary: Machine learning is the data-driven process of constructing mathematical models that can be predictive of data observed in the future. In this course, we will study the use of a range of supervised, semi-supervised, and unsupervised methods to solve both classification and regression problems.
Instructor: Yifu Li
Summary: Machine Learning for Data Science provides a broad overview of widely accepted and state-of-the-art machine learning approaches to automatically extract information from a variety of data types. This course will include conceptual background on data, methods, and application approaches; coverage of issues of data security, privacy, and ethics related to machine learning; and practical, hands-on exercises.
Instructor: Golnaz Habibi
Summary: This course delves into the foundational principles of mathematics and the practical implementation of state-of-the-art autonomous navigation algorithms, specifically within the domains of self-driving cars, delivery robots, and autonomous aerial vehicles such as drones. Positioned within the field of robotics, it explores various aspects of autonomous navigation, covering motion planning, computer vision, localization, and mapping.
Summary: History of intelligent robotics; functional models approach; reactive robots; ethology for robotics; architectures and methodologies; implementation; sensing; hybrid deliberative/reactive robotics; multi-robot systems; navigation; topological path planning; metric path planning; localization and mapping.
