切里顿计算机科学学院以David R. Cheriton的名字命名
The David R. Cheriton School of Computer Science has an international reputation in teaching, academics, research, and employment. We attract exceptional students from all over the world to study and conduct research with our award-winning faculty. You can participate in research projects in a wide variety of topics with our internationally acclaimed researchers. Our research spans the field of computer science, from core work on systems, theory and programming languages to human-computer interaction, DNA and quantum computing to theoretical and applied machine learning, just to name a few. As a graduate student, you will: Access research-intensive lab spaces. Gain the opportunity to publish your work in top conferences and journals. Present at premier conferences in front of peers, industry leaders, researchers, and experts in your field. As a graduate student, you will have the independence to pursue your preferred area of research with a faculty If you want to continue pursuing research and expand your learning, you will work with a supervisor to develop a thesis. As a graduate student at the PhD level you will be expected to conduct meaningful research that expands the scope of your graduate work.<br><br>Artificial intelligence and machine learning are broad research areas within computer science that encompass a number of topics related to the design of computer systems that perform tasks conventionally associated with human intelligence. These areas overlap with several other research areas both within and beyond computer science, including algorithm design, information theory, statistics, optimization, scientific computation, human-computer interaction, and more. <br>Machine learning is concerned with the analysis and development of methods to explore, discover, visualize, and model structure in data as well as to make predictions and decisions based on that structure. Data is often incomplete, noisy, non-homogeneous in structure and large in size (e.g., large number of observations or dimensions, or both). Special attention is paid to the development of computationally efficient (with respect to time and memory usage) algorithms. Research includes the mathematical and computational analysis of the statistical methodology, the development of new techniques, algorithms, and software, and the application of these to complex problems from other areas.