This laboratory course introduces the statistical computing language R and provides hands-on experience using R to screen and adjust data, examine deterministic functions and probability distributions, conduct classic one- and two-sample tests, utilize bootstrapping and Monte Carlo randomization procedures, and conduct stochastic simulations for ecological modeling.

This course provides students with an understanding of basic statistical concepts critical to the proper use and understanding of statistics in ecology and conservation science and prepares students for subsequent ECO courses in ecological modeling.

Introduction to the research process in the natural resources sciences. Focus on research philosophy, concepts, and design, progressing from development of hypotheses, questions, and proposals, to grants and budgeting, and delivery of such research products as reports, publications, and presentations.

Not available at this time

Not available at this time

Not available at this time

Not available at this time

Machine learning is employed in an increasingly wide range of applications. This course will cover two sides about machine learning. On one side, we will talk about recent systems research in machine learning, such as efficient training and inference, distributed and parallel learning systems, and debugging and profiling of ML applications. On the other side, we will discuss research in using machine learning for systems, e.g., identifying performance, reliability, and scalability issues.

This course introduces the students to the design of embedded systems with a focus in unprecedented cyber-physical systems and internet of things applications. It takes a holistic approach to design end-to-end systems by addressing challenges at the hardware, software, and network layers of the stack. Special attention is paid to design trustworthy systems for applications running on commodity platforms and operating systems.