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CS 6550: Advanced DBMS

3.00 Credits

Weber State University

The course covers design, management, implementation, and programming of relational database systems in a high-volume data environment. Database design principles like E-R modeling, schema design and refinement, normal forms, and constraints are introduced. Data Definition Language queries and Data Manipulation Language queries are covered to implement and query a database. Other SQL implementation topics like stored procedures, functions, triggers, and indexes are covered. Advanced database topics like big data analytics, storage management principles, query processing and optimization, transaction management, concurrency control, parallel and distributed databases are covered. The course also introduces alternatives to relational databases. Prerequisite: CS 2550

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CS 6570: Data Science Algorithms I

3.00 Credits

Weber State University

This course is the first in a two-course sequence that prepares students to apply practical solutions to modern data science problems as a professional data analyst. The data management, storage, and manipulation common in data science are taught along with applied machine learning and statistics. Topics include, but are not limited to, the following: data wrangling, feature reduction, data visualization, descriptive and inferential statistics, applied statistical models, applied machine learning, natural language processing, prediction algorithms, and forecasting.

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CS 6580: Data Science Algorithms ll

3.00 Credits

Weber State University

This course is the second in a two-course sequence that prepares students to apply practical solutions to modern data science problems as a professional data analyst. The intent of this course is to go further in-depth and reinforce the concepts taught in CS 6570. Students will investigate and analyze the topics of CS 6570 using advanced tools and techniques to better prepare them for the challenges associated with the data science discipline. The principles of visualization will also be emphasized as well as exposure to recommendation engines and time series forecasting and analysis. Prerequisite: CS 4580 and CS 6570

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CS 6600: Machine Learning

3.00 Credits

Weber State University

Introduction to fundamental principles and practical techniques of machine learning and its applications, including parametric and non-parametric algorithms, support vector machines, kernels, neural networks, clustering algorithms, dimensionality reduction, recommender systems, and deep learning. This course focuses on understanding and implementing the machine learning algorithms. Prerequisite: CS 3100

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CS 6610: Computer Architecture

3.00 Credits

Weber State University

Investigation of high-performance computer processing architectures, including concurrent, multicore platforms; memory hierarchy; static and dynamic scheduling; instruction-level parallelism, including branch prediction; graphics processing units; cache performance and analysis. Prerequisite: CS 2810 and ECE 3710

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CS 6650: Interaction Design

3.00 Credits

Weber State University

The most exciting technologies of today are immersive, interactive, solve big problems, and are even entertaining. In this course, we will study how, as software developers, we can understand our users and create innovative designs that best meet their needs and desires. Known tools and techniques from the field of human-computer interaction are reviewed. User center research and evaluation techniques will be presented and students will have the opportunity to undertake a study on user design where they apply these techniques. Prerequisite: CS 3100

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CS 6700: Deep Learning Theory

3.00 Credits

Weber State University

Deep learning is at the heart of many life-changing applications and areas of interests including language understanding, face recognition, speech synthesis and recognition, object detection, and robotics to name a few. This course introduces the fundamental principles of deep learning and its applications, including multilayer perceptrons, backpropagation, auto-differentiation, optimizers, convolutional networks (CNNs), recurrent networks (RNNs), autoencoders (AEs), and generative adversarial networks (GANs). This course focuses on both understanding deep learning algorithms (their strengths and limitations) and getting acquainted with the current deep learning research landscape. Prerequisite: CS 6600

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CS 6705: Applied Cloud Computing

3.00 Credits

Weber State University

Current business demands often require an amount of data that cannot reasonably process on a single computer. Even companies that work with reasonably small datasets expect rapid growth, so they prefer to use data processing solutions that scale when needed. In this course, you will gain practical, hands-on experience with modern cloud computing resources through publicly available cloud infrastructures. This course will prepare students with practical, hands-on experience in modern cloud and distributed computing paradigms and tools. Prerequisite: CS 3580 and CS 6570

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CS 6710: Software Testing

3.00 Credits

Weber State University

Introduction to fundamental principles and processes of software testing, including testing throughout the software development lifecycle, static testing, testing techniques and management; develop and apply knowledge and skills in implementing testing techniques in software development projects; including black-box and white-box testing, desktop, web, and mobile testing, testing automation, and test metrics.

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CS 6720: Program Debugging and Repair

3.00 Credits

Weber State University

Introduction to software testing as a precursor of debugging and repair. Understanding the cognitive process behind debugging and an introduction to scientific debugging. Introduction to automated debugging techniques like Fault Localization and Delta Debugging. Understanding the intuition behind Automatic Program Repair (APR). Introduction to APR techniques and tools. Introduction to recent advances in program debugging and repair. Understanding challenges and opportunities associated with automated program debugging and repair.

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