Enrollment and waitlist data for current and upcoming courses refresh every 10 minutes; all other information as of 6:00 AM.
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21102
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 230
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21332
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
6:00 pm 6:00 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21104
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21103
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21105
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21129
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 327
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21131
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21132
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 325
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21322
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 325
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21323
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 602 or SEIS 604
3 Credits
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21133
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21134
CoFlex:In Person&Online Sync | Lecture
St Paul: Owens Science Hall 275
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21135
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
6:00 pm 6:00 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21136
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
6:00 pm 6:00 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21137
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21138
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 328
Online
Requirements Met:
LLM/MSL Elective
Even the most insightful data analysis has limited value if analysts cannot convey clear, actionable insights to non-technical audiences. This course develops the critical skills necessary to transform complex quantitative findings into compelling data stories and visualizations. Students will learn how to leverage visual design principles that speak directly to human cognitive abilities, guiding business stakeholders toward data-driven decisions. The curriculum covers creating meaningful graphs, reports, and dashboards that improve comprehension, catalyze communication, and enable fact-based choices. By mastering techniques for visualizing and explaining data, students will become adept at distilling analytical conclusions into incisive narratives readily grasped by diverse audiences. Upon completion, they will have obtained hands-on experience with state-of-the-art data visualization tools to generate impactful data-driven visual insights.
3 Credits
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21139
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 314
Online
Requirements Met:
LLM/MSL Elective
Even the most insightful data analysis has limited value if analysts cannot convey clear, actionable insights to non-technical audiences. This course develops the critical skills necessary to transform complex quantitative findings into compelling data stories and visualizations. Students will learn how to leverage visual design principles that speak directly to human cognitive abilities, guiding business stakeholders toward data-driven decisions. The curriculum covers creating meaningful graphs, reports, and dashboards that improve comprehension, catalyze communication, and enable fact-based choices. By mastering techniques for visualizing and explaining data, students will become adept at distilling analytical conclusions into incisive narratives readily grasped by diverse audiences. Upon completion, they will have obtained hands-on experience with state-of-the-art data visualization tools to generate impactful data-driven visual insights.
3 Credits
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 22996
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 313
Online
Requirements Met:
LLM/MSL Elective
Even the most insightful data analysis has limited value if analysts cannot convey clear, actionable insights to non-technical audiences. This course develops the critical skills necessary to transform complex quantitative findings into compelling data stories and visualizations. Students will learn how to leverage visual design principles that speak directly to human cognitive abilities, guiding business stakeholders toward data-driven decisions. The curriculum covers creating meaningful graphs, reports, and dashboards that improve comprehension, catalyze communication, and enable fact-based choices. By mastering techniques for visualizing and explaining data, students will become adept at distilling analytical conclusions into incisive narratives readily grasped by diverse audiences. Upon completion, they will have obtained hands-on experience with state-of-the-art data visualization tools to generate impactful data-driven visual insights.
3 Credits
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21456
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: SEIS 601 or SIES 603
3 Credits
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21835
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 22539
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21140
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 327
Online
This overview course will provide the foundation of information technology security, including authentication, authorization, access management, physical security, network security (firewalls, intrusion detection), application security (software and database), digital privacy, technology risk management, regulatory compliance, and security operations (e.g., incident response, monitoring, continuity). We will explore social engineering and other human factors and the impact to security.
3 Credits
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21145
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 329
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21143
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 310
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
In today’s data-driven world, Data Scientists and Data Engineers must have a solid understanding of data warehousing concepts. Many of the most valuable data sets still reside in corporate data warehouses. While the fundamental principles of data warehousing have existed for decades, a growing number of companies are now migrating these workloads to the cloud. This course aims to provide students with hands-on experience using popular cloud-based tools and data formats to develop metrics and features for analytics and machine learning. To achieve this, the course will begin by exploring the design differences between relational systems and data warehouses. It will then delve into best practices and common challenges associated with working with data from various sources. Additionally, as enterprises increasingly invest in data governance, data lineage, and master and metadata management to preserve contextual information, these concepts will also be covered. Understanding these topics is essential for leveraging disparate sources of information effectively.
3 Credits
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21144
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 310
Online
Requirements Met:
Software Data Mgmt Conc
Software Technical Elective
In today’s data-driven world, Data Scientists and Data Engineers must have a solid understanding of data warehousing concepts. Many of the most valuable data sets still reside in corporate data warehouses. While the fundamental principles of data warehousing have existed for decades, a growing number of companies are now migrating these workloads to the cloud. This course aims to provide students with hands-on experience using popular cloud-based tools and data formats to develop metrics and features for analytics and machine learning. To achieve this, the course will begin by exploring the design differences between relational systems and data warehouses. It will then delve into best practices and common challenges associated with working with data from various sources. Additionally, as enterprises increasingly invest in data governance, data lineage, and master and metadata management to preserve contextual information, these concepts will also be covered. Understanding these topics is essential for leveraging disparate sources of information effectively.
3 Credits
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 21327
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 327
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 21451
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 328
Online
In today's data world, there are many ways to store data - as the type of data collected globally becomes vast, the need to store and analyze semi-structured or unstructured data becomes more commonplace. The Data Lakes and Advanced Analytics course will teach students how to extract, load, and transform data in a data lake with hands-on experience using Databricks. By the end of the program, students should be comfortable pulling everything from basic reporting to building business intelligence visualizations and dashboards. The course will also introduce Databricks' capabilities to AI & ML. Throughout the course, students will also be exposed to data strategy concepts encompassing topics such as data governance, master data management, medallion layering, and self-service reporting. Prerequisites: SEIS 603 and SEIS 630
3 Credits
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 21331
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 230
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 21146
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 22553
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 408
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 21148
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 22554
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 230
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 21836
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21837
Online: Sync Distributed | Online: Synchronous
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
| 02/03 - 05/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 21838
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 308
Online
This course will explore the dynamic intersection of machine intelligence and human conversation. Throughout this course, you'll discover the profound practical benefits of Conversational AI. Businesses can revamp their approach to customer communication, leading to instant query resolution and increased customer loyalty. If you're inclined towards data, you'll appreciate how Conversational AI can simplify complex data sets, pulling out meaningful insights faster than ever. Consider the significant boost in productivity for general workplace scenarios when intuitive AI systems handle routine tasks, such as scheduling and information retrieval. We've structured this course to give you both a solid grounding in the theoretical aspects of Conversational AI and hands-on experience with its real-world applications. Whether you aim to refine customer interactions in a business setting, optimize data analysis, or enhance workplace productivity, this course promises to be transformative. Get ready to delve deep; by the end, students will be well-equipped to lead the charge in shaping the future of communication and productivity. Prerequisite: SEIS 764 Artificial Intelligence
3 Credits