Prepares students to use logic as a formal tool to solve problems in computer science and AI. No previous exposure to machine learning is required.ĬS 474 Logic in Computer Science credit: 3 or 4 Hours.Īn introduction to mathematical logic from the perspective of computer science, emphasizing both computable aspects of logic, especially automated reasoning, as well as applications of logic to computer science in artificial intelligence, databases, formal methods, and theoretical computer science.
Prerequisite: Multi-variable calculus, linear algebra ( MATH 225 or MATH 257 or MATH 415 or MATH 416 or ASRM 406), data structures ( CS 225 or equivalent), CS 361 or STAT 400. Those registered for 4 credit hours will have to complete a project. Coursework will consist of programming assignments in a common deep learning framework. Topics include: linear classifiers multi-layer neural networks back-propagation and stochastic gradient descent convolutional neural networks and their applications to object detection and dense image labeling recurrent neural networks and state-of-the-art sequence models like transformers generative adversarial networks and variational autoencoders for image generation and deep reinforcement learning. Provides an elementary hands-on introduction to neural networks and deep learning with an emphasis on computer vision applications. Prerequisite: CS 225 one of CS 440, CS 441 or CS 446 one of MATH 225, MATH 257, MATH 415, MATH 416 or ASRM 406.ĬS 444 Deep Learning for Computer Vision credit: 3 or 4 Hours. Students registered for 4 credit hours will also finish a final project based on the class topics, demonstrating their ability to propose related new algorithms based on the class subjects. Students will be required to finish three related homework projects, including 1) developing a machine learning classifier, 2) designing adversarial attacks against the built classifier, and 3) developing defenses to improve the robustness of the trained classifier against designed attacks. Students will learn to analyze current interactions between attackers and defenders on machine learning and therefore develop an understanding of the principles on trustworthy machine learning which is an emerging and important topic.
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The lessons are reinforced via a series of topic-driven lectures, coding assignments, related paper readings, exams and in-class discussions. Students will explore topics including basic machine learning foundations (e.g., linear regression and PCA), adversarial attacks against different learning algorithms, differential privacy, data valuation, and different categories of defenses.
Prepares students to understand the security and privacy problems in machine learning and educates students to propose different attack strategies to identify the vulnerabilities of a range of learning algorithms and understand different defense approaches towards trustworthy machine learning systems. Prerequisite: CS 225 and CS 361.ĬS 442 Trustworthy Machine Learning credit: 3 or 4 Hours. Application areas include computer vision, natural language, interpreting accelerometer data, and understanding audio data. The course will focus on tool-oriented and problem-oriented exposition. Techniques of machine learning to various signal problems: regression, including linear regression, multiple regression, regression forest and nearest neighbors regression classification with various methods, including logistic regression, support vector machines, nearest neighbors, simple boosting and decision forests clustering with various methods, including basic agglomerative clustering and k-means resampling methods, including cross-validation and the bootstrap model selection methods, including AIC, stepwise selection and the lasso hidden Markov models model estimation in the presence of missing variables and neural networks, including deep networks. CS 441 Applied Machine Learning credit: 3 or 4 Hours.
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