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TESOL 503: Structure of English Dr. Robin Barr Office hours Tuesdays & Thursdays after 8 p.m. Description: In order to teach a language, it is not enough to know how to speak it you need to have a

This course will introduce important concepts, like nominalization and declension, while applying them to the language. You will learn all the grammatical endings of the verb-form and its three-syllable prefixes, like in and an. You will also learn the basic structures of sentence construction including the use of the subordination rules. The end aims of this course is for you to be conversing or writing English as a native speaker in a native-speaking environment. Prerequisites: CS 510 or Instructor Permission CS 509/CS 510: Introduction to Computer Security For those who are involved in computer security or would like an introduction to the area. Topics will include cryptographic theory, algorithms, protocols, privacy and anonymity concerns. In this course you will be introduced to the areas of information security, the principles and practices of cryptography and computer security through lectures, demonstrations, problem-solving problem sets, discussion boards, and hands-on projects. Prerequisites: CS 510 or Instructor Permission CS 520: Computational Image Analysis and Machine Learning This course is designed for students who wish to develop a theoretical and practical understanding the concepts of computational image analysis and machine learning. We will cover the structure of image data and the computational image analysis techniques used in computer vision. We will develop a theoretical viewpoint on the problem of designing neural net models for image perception. Furthermore, we will cover the algorithms used in classification and regression. The last 3 weeks will consist of practical exercises in image and data processing. Prerequisites: CS 520 or instructor permission. CS 530: Machine Learning and Decision Making We cover the principles and algorithms required for building large-scale deep learning models. We build systems with tens of thousands of users and millions of training images that are given feedback. By training a deep neural network on a training set of images we will make decisions. The learning will start from the input and progress outward through all layers until reaching a decision tree. The decision tree can be used to predict the user future response to particular tasks. Prerequisites: CS 520 recommended. CS 535: Introduction to Computational Science for Computer Scientists A course for computational scientists and engineers to develop an understanding of the mathematical foundations and conceptual tools necessary for computational science and engineering research to be successful. Topics include the theory, foundations, and applications of computational probability. Topics include computational complexity, the Turing test, the halting problem, machine learning, and statistical inference.

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The deadline to file TESOL 503 structure in 2023 is June 30th.

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