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Computer Science Courses

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C S 580 - Theory of Predictive Modeling

Mathematical, computational, and philosophical foundations of machine learning, control, and physical modeling. Introduction to system identification, causality, uncertainty, model approximation, and information geometry.
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C S 799 R - Doctoral Dissertation

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C S 795 R - Teaching Practicum

Curricula, principles, concepts and experiences in teaching computer science effectively.
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C S 786 R - Readings in Model Checking

Focused readings and projects as suggested by class interest and current trends.
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C S 778 R - Topics Neural Nets

Advanced topics and readings in neural networks and machine learning.
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C S 765 R - Adv Topics in Comptr Security

Advanced topics and reading in computer security.
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C S 755 R - Topics in Computer Graphics

Latest research in the field of computer graphics. Recent papers read, presented, and discussed.
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C S 751 R - Adv Database Sstms

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C S 712 R - Algorithmic Decision Processes

Latest research in the field of algorithmic decision processes. Recent papers read, presented, and discussed.
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C S 704 R - Advanced Readings

Find, read, evaluate and write about ideas in research papers in a specific computer science topic. Instructor sets the topic for the course.
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C S 699 R - Master's Thesis

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C S 698 R - Master's Project

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C S 697 R - Graduate Special Projects

Graduate-level special projects and mentored learning experiences.
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C S 686 - Advanced Model Checking

Techniques for managing state explosion and specialized algorithms for verifying classes of infinite state systems.
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C S 678 - Adv Machine Learning Models

Advanced models, algorithms, and approaches in neural networks and machine learning.
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C S 677 - Bayesian Methods in C S

Applying Bayesian methods useful for incorporating confidence or belief into problems in computer science, allowing computers to better handle uncertainty.
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C S 676 - Adv Topics in Data Mining

Data mining process, data warehousing concepts, text mining, ethical issues, meta-learning, and key success factors.
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C S 674 - Advanced Deep Learning

In-depth examination of the mathematical foundations of modern deep learning, surveying current research in the area. Topics include stochastic and distributed optimization, regularization, initialization, network architecture design, and loss function design. Concepts are developed in the context of various application areas including supervised learning, generative modeling, and reinforcement learning.
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C S 673 - Computational Creativity

Project-based course for understanding creativity as a way to increase the robustness of intelligent systems while applying AI/ML tools to challenging problems.
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C S 670 - Multi-Agent Systems

Introduction to fundamental concepts emphasizing current literature. Topics include game theory, repeated play games, Arrow's impossibility theorem, negotiation, search, and learning.
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C S 665 - Adv Comp Security

Authentication and authorization using digital credentials.
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C S 660 - Adv Computer Networks

Computer networking, software architecture, organization, protocols, routing, global networks, local networks, internet standards, and applications.
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C S 656 - Interactive Software Systems

Fabrication of interactive experiences in the context of emerging technologies; recent advances in human-computer interaction.
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C S 655 - Advanced Computer Graphics

Advanced computer graphics systems programming and architecture, including ray tracing, radiosity, animation, and physically based modeling.
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C S 653 - Information Retrieval

IR modeling, IR query languages, text indexing and searching, retrieval evaluation, query and text operations, parallel and distributed IR, Web searching.
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C S 651 - Comp Inference & Recognition

Mathematical representations and modern Machine Learning techniques for the interpretation and recognition of Visual Data (images, video, etc.).
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C S 650 R - Advanced Computer Vision 1

Advanced topics in computer vision reflecting current areas of interest in the field and related applications.
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C S 618 - Computational Biology

Algorithms for DNA sequence analysis. Heuristics analyzed and developed for NP-complete problems including alignment, phylogeny, secondary structure predictions, protein folding, and microarray analysis.
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C S 611 - Theoretical Fndtns of Computng

Proofs (deductive and inductive reasoning), computability (models of computability and computability issues), and complexity (time and space bounds, nondeterminism, and complexity classification).
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C S 601 R - Graduate Topics in C S

Graduate-level subjects and new graduate courses as announced before each semester.
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