Enrollment and waitlist data for current and upcoming courses refresh every 10 minutes; all other information as of 6:00 AM.
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21338
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 328
Online
This is a foundational software development course focusing on fundamental programming concepts as implemented using the Java programming language. These concepts include general problem solving and algorithm creation techniques, primitive and object data types, constants, variables, expressions, and boolean logic and control flow. In addition, we will discuss fundamental object-oriented concepts, such as objects and classes, object instantiation and initialization, method implementation and invocation, interfaces, inheritance, and garbage collection. Students will apply these concepts by writing programs in the Java programming language. JUnit will be discussed for Unit and Integration Testing.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21852
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 328
Online
This is a foundational software development course focusing on intermediate-level fundamental and foundational concepts. Abstract data type concepts will be discussed in detail. Data Structures and some of their associated algorithms for Algorithm Analysis will be discussed. Canonical implementations and framework supplied implementation alternatives (such as the JDK or other framework alternatives) will be explored and used as well. To apply the lecture concepts, we will implement software using the Java programming language and explore some of the tools used by software developers. Eclipse would be used as an integrated development environment for code development. Further, tools for managing software build, configuration, and version control (e.g., Git) and unit and integration testing (e.g., JUnit) will be used. We will also discuss multi-threading, memory management, refactoring, and advanced debugging techniques. Prerequisite: SEIS 601 or equivalent
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
6:00 pm 6:00 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21340
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 333
Online
This is an introductory software development course with a focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. PyTest will be discussed for Unit and Integration Testing.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21339
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 325
Online
This is an introductory software development course with a focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. PyTest will be discussed for Unit and Integration Testing.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21341
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 325
Online
This is an introductory software development course with a focus on fundamental and foundational concepts. These concepts include general problem solving and algorithm creation techniques, data types, constants, variables and expressions, boolean, control flow, and object-oriented concepts. Applying these concepts, we implement programs using the Python language. We will examine its use as an interpreted and a compiled language, working with data types such as numbers, strings, lists, dictionaries, and sets. Students will learn how to apply Python in managing data. PyTest will be discussed for Unit and Integration Testing.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21835
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 325
Online
This is a foundational software development course focusing on intermediate-level fundamental and foundational concepts. Abstract data type concepts will be discussed in detail. Data Structures and some of their associated algorithms for Algorithm Analysis will be discussed. Canonical implementations and framework supplied implementation alternatives will be explored and used as well. To apply the lecture concepts, we will implement software using the Python programming language and explore some of the tools used by software developers. Spyder or PyCharm would be used as integrated development environments (IDE) for code development. Further, tools for managing software build, configuration, and version control (e.g., Git) and unit and integration testing (e.g., PyTest) will be used. We will also discuss multi-threading, memory management, refactoring, and advanced debugging techniques. Prerequisites: SEIS 603
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21371
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 408
Online
This introductory course covers software engineering concepts, techniques, and methodologies. The course introduces software engineering life-cycle models, such as Scrum and Kanban. Students learn the essential concepts of different lifecycle models and where their application is appropriate. The course continues by teaching concepts of requirements acquisition and various methods of requirements refinement. Also presented in this course are concepts of object-oriented and structured design. The course incorporates vital supporting topics such as software metrics, project planning, cost estimation, software maintenance, and an introduction to data structures and running time analysis. Prerequisite: SEIS 601 or SEIS 603. SEIS 610 can be taken concurrently with SEIS 601 or SEIS 603.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21373
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 326
Online
This course covers the fundamentals of IT infrastructure in the cloud. It provides a detailed overview of cloud concepts, services, security, architecture, and economics. This course will examine the theory behind these modern practices and the real-world implementation challenges faced by IT organizations. Students will learn how to design and implement cloud-based solutions. While the lessons will cover a number of theoretical concepts, we will primarily learn by doing. Students will gain hands-on experience with several widely-adopted IT platforms including AWS and Docker.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21374
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 326
Online
This course covers the fundamentals of IT infrastructure in the cloud. It provides a detailed overview of cloud concepts, services, security, architecture, and economics. This course will examine the theory behind these modern practices and the real-world implementation challenges faced by IT organizations. Students will learn how to design and implement cloud-based solutions. While the lessons will cover a number of theoretical concepts, we will primarily learn by doing. Students will gain hands-on experience with several widely-adopted IT platforms including AWS and Docker.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21837
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 326
Online
This course covers the engineering and design of IT infrastructure, focusing on infrastructure as Code practices. IT infrastructure deployment practices are rapidly changing as organizations build infrastructure as code and adopt cloud computing platforms. We will examine the theory behind these modern practices and the real-world implementation challenges faced by IT organizations. The lessons will cover a number of tools, techniques, and patterns to implement infrastructure as code. Students will learn about platforms and tooling involved in creating and configuring infrastructure elements, patterns for using these tools, and practices for making infrastructure as code work in production. Prerequisites: SEIS 615
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21838
Online: Sync Distributed | Lecture
Online
This course will teach students the essentials of becoming a full stack web developer by creating dynamic, interactive websites, and is suitable for anyone with basic computer programming skills. The course initially focuses on HTML, CSS and JavaScript and later transactions into technologies like Angular framework, Node, and Serverless functions in a cloud environment. Students develop skills for designing, publishing, and maintaining websites for professional or personal use. No previous experience or knowledge of web development is needed. Prerequisites: SEIS 601 or 603
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21376
Online: Sync Distributed | Lecture
Online
This course will provide students with a comprehensive overview of the principles, processes, and practices of many available agile software product development techniques. Students will learn agile planning, development, and delivery techniques with Scrum, Kanban, Lean, Extreme, Crystal, Dynamic, and Feature Driven Development. Scaled agile framework (SAFe) for large enterprises in scaling lean and agile practices beyond a single team along with Large-scale Scrum (LeSS) and disciplined agile delivery (DAD) will also be explored. Students will be provided with the opportunity to apply the skills in creating and delivering new products in a team environment. Drivers behind agility in software development along with methods for project tracking, project communication, team collaboration, client relationship management, stakeholder management and quality of deliverables will be discussed at length.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21377
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 313
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
This course focuses on database management system concepts, database design, and implementation. Conceptual data modeling using Entity Relationships (ER) is used to capture the requirements of a database design. Relational model concepts are introduced and mapping from ER to relational model is discussed. Logical database design, normalization, and indexing strategies are also discussed to aid system performance. Structured Query Language (SQL) is used to work with a database using the Oracle platform. The course also covers query optimization and execution strategies, concurrency control, locking, deadlocks, security, and backup/recovery concepts. Non-relational databases are also briefly introduced. Students will use Oracle and/or SQL Server to design and create a database using SQL as their project.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21378
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 329
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
This course focuses on database management system concepts, database design, and implementation. Conceptual data modeling using Entity Relationships (ER) is used to capture the requirements of a database design. Relational model concepts are introduced and mapping from ER to relational model is discussed. Logical database design, normalization, and indexing strategies are also discussed to aid system performance. Structured Query Language (SQL) is used to work with a database using the Oracle platform. The course also covers query optimization and execution strategies, concurrency control, locking, deadlocks, security, and backup/recovery concepts. Non-relational databases are also briefly introduced. Students will use Oracle and/or SQL Server to design and create a database using SQL as their project.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
6:00 pm 6:00 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21380
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 333
Online
This course provides a broad introduction to the subject of data analysis by introducing common techniques that are essential for analyzing and deriving meaningful information from datasets. In particular, the course will focus on relevant methods for performing data collection, representation, transformation, and data-driven decision making. The course will introduce students to Statistical Science including Probability Distribution, Sampling Distribution, Statistical Inference, and Significance Testing. Students will also develop proficiency in the widely used Python language which will be used throughout the course to reinforce the topics covered. Packages like NumPy and Pandas will be discussed at length for Data Cleaning, Data Wrangling: Joins, Combine, Data Reshape, Data Aggregation, Group Operation, and Time Series analysis. Prerequisite: SEIS 603
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
6:00 pm 6:00 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21381
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 333
Online
This course provides a broad introduction to the subject of data analysis by introducing common techniques that are essential for analyzing and deriving meaningful information from datasets. In particular, the course will focus on relevant methods for performing data collection, representation, transformation, and data-driven decision making. The course will introduce students to Statistical Science including Probability Distribution, Sampling Distribution, Statistical Inference, and Significance Testing. Students will also develop proficiency in the widely used Python language which will be used throughout the course to reinforce the topics covered. Packages like NumPy and Pandas will be discussed at length for Data Cleaning, Data Wrangling: Joins, Combine, Data Reshape, Data Aggregation, Group Operation, and Time Series analysis. Prerequisite: SEIS 603
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21382
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 313
Online
Requirements Met:
LLM/MSL Elective
The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21383
Online: Sync Distributed | Lecture
Online
Requirements Met:
LLM/MSL Elective
The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 22334
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 408
Online
Requirements Met:
Software Embedded Systems Conc
Software Comp Security Cert
Software Technical Elective
This course introduces the basic concepts involved in ethical hacking. An ethical hacker assesses software security by looking for weaknesses and vulnerabilities in target systems. An effective ethical hacker must understand network communications, software development, and operating systems internals. The course begins with a review of the fundamental topics of operating systems design. Topics such as process scheduling, input/output, memory management, file system design, security, and protection mechanisms are covered. The course continues with activities performed by ethical hackers, such as testing via injection attacks, searching for broken authentication, identifying security misconfigurations, and pinpointing data exposure. Prerequisites: None.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 22748
Online: Sync Distributed | Lecture
Online
The purpose of this course is to guide students through the knowledge, skills, and opportunities needed to develop an ethical foundation on which they can build their careers as AI practitioners or as professionals in other fields that have been or will be impacted by AI. We will explore a variety of ethical issues related to the development and use of AI across multiple fields of study, with an emphasis on the human impact of AI. Course topics will cover a range of foundational AI concepts including data preparation, bias, neural networks, natural language processing, large language models, generative AI, model validation, and more, in the context of issues like discrimination, misinformation, intellectual property, regulation, jobs, and humanity at large. Class sessions are comprised of a weekly “hot topic” where we will explore the ethical implications of current events in AI, a lecture period, and lab where students have the opportunity to discuss and apply the course material to practical and theoretical exercises. This course is intended for both technical and non-technical audiences.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21393
Online: Sync Distributed | Lecture
Online
Digital transformation promises a bridge to a digital future, where organizations can thrive more fluid business models and processes. Less than 20% of organizations are getting digital transformations right, but these digitally transformed organizations can deliver twice as fast as other organizations. Large language models (LLMs) and ChatGPT, automation and AI will supercharge further change into a second chapter of radical change. Digital Transformation 2.0 is an innovative course that delves into the world of digital transformation, focusing on the new change, the Future of Work and the impact of ChatGPT and Generative AI technologies on modern businesses and industries. This course provides students with hands-on experience using ChatGPT and other AI tools while exploring digital maturity models and the establishment of a Generative AI Center of Excellence (GAICoE). Students will learn how to integrate AI-driven solutions into business processes and strategies, transforming the way organizations operate in the digital age.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21387
Online: Sync Distributed | Lecture
Online
This course provides students with a theoretical and practical understanding of Strategy and Enterprise Architecture (EA). It studies how EA enables organizations to effectively accomplish their business goals. Specifically, the course analyzes the relationships among business strategies, IT strategies, business, applications, information, and technology architectures. It also examines current industry trends such as: design thinking, digital transformation, cloud migration, and introduces students to EA implementation frameworks and tools.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21389
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 331
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
The real world is messy and a data scientist’s job will be to make sense of it. This course will dive into specialized data formats, such as time series, geospatial data, semi-structured and the data management systems and considerations required to load and extract information from them. Leveraging both creativity and context data scientists can design highly impactful features for machine learning applications by using SQL and Python to transform data. This course aims to provide hands-on experience working with these data formats and the power of developing novel metrics and features for analytics and machine learning. To do this effectively, this course will compare and contrast the conceptual designs of relational, data warehouse, NoSQL, and other data systems so that practitioners can utilize these systems to their fullest. Lastly, enterprises are investing heavily in data governance, data lineage, and metadata management to better preserve contextual information about their data. These systems will be covered as they will increasingly be required to enable disparate sources of information to be leveraged together and crucial for data scientists to build accurate and ethical models for deployment. Prerequisites: SEIS 630 and SEIS 631
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21390
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 331
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
The real world is messy and a data scientist’s job will be to make sense of it. This course will dive into specialized data formats, such as time series, geospatial data, semi-structured and the data management systems and considerations required to load and extract information from them. Leveraging both creativity and context data scientists can design highly impactful features for machine learning applications by using SQL and Python to transform data. This course aims to provide hands-on experience working with these data formats and the power of developing novel metrics and features for analytics and machine learning. To do this effectively, this course will compare and contrast the conceptual designs of relational, data warehouse, NoSQL, and other data systems so that practitioners can utilize these systems to their fullest. Lastly, enterprises are investing heavily in data governance, data lineage, and metadata management to better preserve contextual information about their data. These systems will be covered as they will increasingly be required to enable disparate sources of information to be leveraged together and crucial for data scientists to build accurate and ethical models for deployment. Prerequisites: SEIS 630 and SEIS 631
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21843
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 408
Online
The course is a unique culmination of software development practices taught in the Master of Software Engineering program and provides students an opportunity to create and showcase a capstone project by implementing a full-stack application. This capstone class provides Software Engineering students with the unique opportunity to conceptualize, design, and implement a project related to their chosen domain. During the project, students build competence in a modern interactive and incremental development methodology; students will refine their acquisition skills and analysis of program requirements. Students will also learn software design patterns and create sophisticated architectural and operational diagrams. Automated software tests will be run, and continuous integration deployment principles will be performed. Prerequisite: SEIS 602, and SEIS 610, and SEIS 622
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 22327
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 331
Online
Individuals generate more data than ever before as they interact with websites, social platforms, streaming services, and increasingly data-driven industries like healthcare, retail, and energy. A growing number of connected devices continuously stream data using familiar web protocols and patterns. In our increasingly digital world, this data is depended upon to drive artificial intelligence and automation in near real-time. Before data can be relied upon to drive AI, however, it must be integrated, carefully curated, and governed at scale. It falls on data engineers to bring together data from various sources and contextualize those datasets to produce intelligence. Massively distributed Data Lake platforms empower engineers to work with datasets at a volume and variety not suitable for traditional, relational databases. This hands-on course focuses on data collection, storage, and analysis on a cloud Data Lake architecture, covering both batch and streaming pipelines. Additionally, it explores NoSQL database paradigms that facilitate low-latency queries over distributed and often unstructured or semi-structured datasets. Expect to learn fundamental concepts and gain practical experience working with different types of data, all within a reliable cloud lab environment. Prerequisites: (SEIS 601 or SEIS 603) and SEIS 630
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 22337
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 333
Online
Individuals generate more data than ever before as they interact with websites, social platforms, streaming services, and increasingly data-driven industries like healthcare, retail, and energy. A growing number of connected devices continuously stream data using familiar web protocols and patterns. In our increasingly digital world, this data is depended upon to drive artificial intelligence and automation in near real-time. Before data can be relied upon to drive AI, however, it must be integrated, carefully curated, and governed at scale. It falls on data engineers to bring together data from various sources and contextualize those datasets to produce intelligence. Massively distributed Data Lake platforms empower engineers to work with datasets at a volume and variety not suitable for traditional, relational databases. This hands-on course focuses on data collection, storage, and analysis on a cloud Data Lake architecture, covering both batch and streaming pipelines. Additionally, it explores NoSQL database paradigms that facilitate low-latency queries over distributed and often unstructured or semi-structured datasets. Expect to learn fundamental concepts and gain practical experience working with different types of data, all within a reliable cloud lab environment. Prerequisites: (SEIS 601 or SEIS 603) and SEIS 630
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 21851
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 327
Online
The course will introduce students to the methods and tools used in User Experience (UX) and User Interface (UI) design. UxDesign will provide an introduction to the foundation of each of the design stage of a product’s lifecycle/journey, and will provide a key understanding on the components required to ensure the end product will meet end user needs. Some of the topics discussed in the course include User Experience Design, Design Thinking, Human Centered Design, UxDesign techniques, such as: personas, user stories / user story mapping, storyboards, wireframing, UxDesign methods, such as: design methods, design prioritization, and rapid/interactive UI development; and coverage of key prototyping tools and software.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 21394
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 314
Online
Machine Learning builds computational systems that learn from and adapt to the data presented to them. It has become one of the essential pillars in information technology today and provides a basis for several applications we use daily in diverse domains such as engineering, medicine, finance, and commerce. This course covers widely used supervised and unsupervised machine learning algorithms used in industry in technical depth, discussing both the theoretical underpinnings of machine learning techniques and providing hands-on experience in implementing them. Additionally, students will also learn to evaluate effectiveness and avoid common pitfalls in applying machine learning to a given problem. Prerequisites: SEIS 631 and 632, 632 can be taken concurrently.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 21395
Online: Sync Distributed | Lecture
Online
Machine Learning builds computational systems that learn from and adapt to the data presented to them. It has become one of the essential pillars in information technology today and provides a basis for several applications we use daily in diverse domains such as engineering, medicine, finance, and commerce. This course covers widely used supervised and unsupervised machine learning algorithms used in industry in technical depth, discussing both the theoretical underpinnings of machine learning techniques and providing hands-on experience in implementing them. Additionally, students will also learn to evaluate effectiveness and avoid common pitfalls in applying machine learning to a given problem. Prerequisites: SEIS 631 and 632, 632 can be taken concurrently.
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 21396
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 314
Online
Artificial Intelligence has made significant strides in recent times and has become ubiquitous in the modern world, impacting our lives in different ways. By harnessing the power of deep neural networks, it is now possible to build real-world intelligent applications that outperform human precision in certain tasks. This course provides a broad coverage of AI techniques with a focus on industry application. Major topics covered in this course include: (1) how deep neural networks learn their intelligence, (2) self-learning from raw data, (3) common training problems and solutions, (4) transferring learning from existing AI systems, (5) training AI systems for machine visions with high accuracy, and (6) training time-series AI systems for recognizing sequential patterns. Students will have hands-on exercises for building efficient AI systems. Prerequisite: SEIS 763
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 22749
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 326
Online
In the rapidly evolving landscape of machine learning and artificial intelligence, the efficient deployment, management, and monitoring of machine learning models are crucial for successful and sustainable outcomes. The Machine Learning Operations (MLOps) course is designed to equip participants with the knowledge and skills needed to bridge the gap between machine learning development and operational deployment. Through a comprehensive curriculum, hands-on labs, and real-world case studies, participants will learn the essential principles and practices that enable seamless collaboration between data scientists, machine learning engineers, and operations teams. This course covers key concepts, tools, and strategies used in MLOps, helping organizations streamline their machine learning pipelines and enhance the reliability, scalability, and maintainability of their models. Prerequisite: SEIS 763
3 Credits
| 02/05 - 05/17 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 22750
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 314
Online
This course offers an interactive learning experience that delves into how machines perceive, analyze, and react to images and visual cues. You'll gain a greater understanding of images, videos, and their processing algorithms through hands-on activities. By working on practical tasks like manipulating images and experimenting with Generative AI models like GANs, you'll discover the vast applications of Vision AI. Industries such as entertainment and healthcare are already benefiting from these technologies, which enable machines to recognize patterns, predict outcomes, and even create art. With this course, you'll learn both the theoretical and practical aspects of Vision AI, empowering you to combine your creativity with cutting-edge technology. At the end of this course, students will develop skill sets in visual intelligence and be poised to shape the future of this exciting field. Prerequisite: SEIS 764 Artificial Intelligence
3 Credits