Advanced Topics in Computer Science Master's Project: Advanced research project in Computer Science.

Advanced Topics in Computer Science Master's Project: Advanced research project in Computer Science.

This CS research writing class uses a workshop format to focus on structure and phrasing while engaging students in a process-based approach to writing. Instruction will emphasize genre and discourse analysis and engage students in activities to strengthen audience awareness. As such, students will analyze representative examples of computer science research writing for stylistic and argumentative conventions and then integrate the awareness of these conventions and "moves" into their own writing. Students will produce or substantially revise a complete piece of writing.

This CS research writing class uses a workshop format to focus on structure and phrasing while engaging students in a process-based approach to writing. Instruction will emphasize genre and discourse analysis and engage students in activities to strengthen audience awareness. As such, students will analyze representative examples of computer science research writing for stylistic and argumentative conventions and then integrate the awareness of these conventions and "moves" into their own writing. Students will produce or substantially revise a complete piece of writing.

Not available at this time

Fundamentals of quantum information systems, including quantum computation, quantum cryptography, and quantum information theory. Topics include: quantum circuit model, qubits, unitary operators, measurement, entanglement, quantum algorithms for factoring and search, quantum key distribution, error-correction and fault-tolerance, information capacity of quantum channels, complexity of quantum computation.

This is a graduate-level introduction to cryptography, with an emphasis on definitions are proofs of security. The viewpoint of the course is "theory applied to practice" in that we attempt to treat topics of direct practical value. Topics covered include: blockciphers, pseudorandom functions and permutations, symmetric-key encryption and modes of operation, hash functions, message authentication codes, authenticated encryption and TLS/SSL, computational algebra and number theory, public-key encryption, digital signatures, and public-key infrastructures.

Machine learning is the computational study of artificial systems that can adapt to novel situations, discover patterns from data, and improve performance with practice. This course will cover the mathematical foundations of supervised and unsupervised learning. The course will provide a state-of-the-art overview of the field, with an emphasis on implementing and deriving learning algorithms for a variety of models from first principles.

This course will provide an introduction to, and comprehensive overview of, reinforcement learning (RL).

This course will focus on modern, practical methods for deep learning with neural networks. The course will begin with a description of simple classifiers such as perceptrons and logistic regression classifiers, and move on to standard neural networks, convolutional neural networks, some elements of recurrent neural networks, and transformers. The emphasis will be on understanding the basics and on practical application more than on theory. Many applications will be in computer vision, but we will make an effort to cover some natural language processing (NLP) applications as well.