Courses

Students are then required to apply their knowledge outside the classroom in a hands-on performance-oriented environment during Leadership LABS and participate in physical fitness training. The lab is 2 hours and the physical fitness training is 3 hours per week. This course is only for contracted cadets.

Students are then required to apply their knowledge outside the classroom in a hands-on performance-oriented environment during Leadership LABS and participate in physical fitness training. The lab is 2 hours and the physical fitness training is 3 hours per week. This course is only for contracted cadets.

This course is designed to provide students with an in-depth exploration of leadership theories and models. Students will create a personal knowledge base from which to create a plan to develop or revise a leadership perspective.

By way of case study and fact pattern analysis, we will find and frame advanced legal and ethical issues presented by past, contemporary and emerging technology. Cases and events will be examined. At the conclusion of the semester, students will be able to identify and discuss legal and ethical issues presented by technology.

This course delves into the advanced principles of machine learning and their applications in security analytics, equipping students with the expertise to tackle modern cybersecurity challenges. Covering topics such as anomaly detection, malware classification, and intrusion detection. The course integrates cutting-edge techniques, including adversarial machine learning, deep learning, and reinforcement learning. Students will gain hands-on experience with popular frameworks.

This course delves into the advanced principles of machine learning and their applications in security analytics, equipping students with the expertise to tackle modern cybersecurity challenges. Covering topics such as anomaly detection, malware classification, and intrusion detection. The course integrates cutting-edge techniques, including adversarial machine learning, deep learning, and reinforcement learning. Students will gain hands-on experience with popular frameworks.

This course provides students with decision theory, estimation, confidence intervals, and hypothesis testing. It introduces large sample theory, asymptotic efficiency of estimates, exponential families, and sequential analysis.

This course provides students with decision theory, estimation, confidence intervals, and hypothesis testing. It introduces large sample theory, asymptotic efficiency of estimates, exponential families, and sequential analysis.

Elements of measurement and evaluation as they relate to research design, statistics and fundamentals of exercise testing. May be taught concurrently with PHED 4183.

Nature and function of measurement in instructional design and development environments. Approaches to reducing test error, including establishing validity and reliability estimates. Item analysis and discrimination. Test development for norm-based and criterion-referenced tests. Knowledge of descriptive statistics required.