Enrollment and waitlist data for current and upcoming courses refresh every 10 minutes; all other information as of 6:00 AM.
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40043
CoFlex:In Person&Online Sync | Lecture
St Paul: Owens Science Hall 275
Online
This foundational software development course focuses on fundamental programming concepts 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 control flow. We discuss object-oriented concepts, such as objects and classes, object instantiation and initialization, method implementation and invocation, interfaces, inheritance, and garbage collection. We will explore how AI assistance can enhance software development through code generation, debugging assistance, and test development. Students will apply these concepts by writing Java programs and unit tests. No prior programming experience is required.
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40447
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 326
Online
This intermediate-level software development course builds upon foundational programming concepts, delving into advanced topics and practical application. We will thoroughly explore abstract data type concepts, providing a deep understanding of data structures and their associated algorithms for algorithm analysis. Canonical implementations and framework-supplied alternatives, such as the JDK and other relevant frameworks, will be examined and utilized. To apply these concepts, we will develop software using the Java programming language, leveraging industry-standard tools. We will also utilize tools for software build management, configuration, and version control (e.g., Git), as well as unit and integration testing (e.g., JUnit). Furthermore, we will discuss multi-threading, memory management, refactoring, and advanced debugging techniques, equipping students with the skills necessary for robust software development. Throughout the course, we will explore how AI assistance can enhance the software development lifecycle. This includes leveraging AI for tasks such as code generation for repetitive patterns, intelligent debugging assistance to identify and resolve complex issues, and automated test development to ensure code reliability. We will also examine how AI can be used to analyze code complexity and suggest refactoring improvements. This course assumes a solid foundation in fundamental software development concepts, including the ability to use and understand the Java programming language. Prerequisite: SEIS 601 or an equivalent understanding of foundational software development concepts is required.
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40217
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 333
Online
This introductory software development course focuses on fundamental programming concepts. We will cover general problem-solving techniques, algorithm creation, data types, constants, variables, expressions, Boolean logic, control flow, and principles of object-oriented programming. Throughout the course, we will implement programs using the Python programming language, exploring its versatility as both an interpreted and a compiled language. Students will work with core data types such as numbers, strings, lists, dictionaries, and sets. They will learn how to use Python for data management, establishing a foundation for future endeavors in fields like data science and web development. Additionally, we will examine how AI-powered tools can enhance the learning and development of Python code. For instance, we will introduce AI-driven code completion and error detection tools to help students understand syntax and debug more effectively. We may also explore AI applications in data analysis and automation, demonstrating potential uses for Python skills. Finally, we will introduce PyTest for unit and integration testing. No prior programming experience in Python or any other programming language is required.
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40218
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 326
Online
This introductory software development course focuses on fundamental programming concepts. We will cover general problem-solving techniques, algorithm creation, data types, constants, variables, expressions, Boolean logic, control flow, and principles of object-oriented programming. Throughout the course, we will implement programs using the Python programming language, exploring its versatility as both an interpreted and a compiled language. Students will work with core data types such as numbers, strings, lists, dictionaries, and sets. They will learn how to use Python for data management, establishing a foundation for future endeavors in fields like data science and web development. Additionally, we will examine how AI-powered tools can enhance the learning and development of Python code. For instance, we will introduce AI-driven code completion and error detection tools to help students understand syntax and debug more effectively. We may also explore AI applications in data analysis and automation, demonstrating potential uses for Python skills. Finally, we will introduce PyTest for unit and integration testing. No prior programming experience in Python or any other programming language is required.
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40219
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 329
Online
This introductory software development course focuses on fundamental programming concepts. We will cover general problem-solving techniques, algorithm creation, data types, constants, variables, expressions, Boolean logic, control flow, and principles of object-oriented programming. Throughout the course, we will implement programs using the Python programming language, exploring its versatility as both an interpreted and a compiled language. Students will work with core data types such as numbers, strings, lists, dictionaries, and sets. They will learn how to use Python for data management, establishing a foundation for future endeavors in fields like data science and web development. Additionally, we will examine how AI-powered tools can enhance the learning and development of Python code. For instance, we will introduce AI-driven code completion and error detection tools to help students understand syntax and debug more effectively. We may also explore AI applications in data analysis and automation, demonstrating potential uses for Python skills. Finally, we will introduce PyTest for unit and integration testing. No prior programming experience in Python or any other programming language is required.
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 42756
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 327
Online
This course explores vibe coding, a novel approach to human–AI pair programming. Students will engage with advanced AI-powered development environments that integrate intelligent assistance directly into the programming workflow. Alongside coding projects, the course first reviews foundational software engineering concepts, including requirements elicitation and requirements refinement. Emphasis is then placed on design patterns, testing, documentation, and ethical and legal issues in AI-assisted coding, as well as best practices for collaborating with AI in real-time. Through lectures, labs, discussions, and a major project, students will develop both the technical and engineering skills necessary to critically and creatively integrate AI into modern software development practices. Prerequisite: SEIS-601 or SEIS-603 or instructors' permission.
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40042
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 333
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. In addition, students explore how emerging approaches such as vibe coding and AI-assisted development can be integrated into traditional software engineering practices. Prerequisite: SEIS 601 or SEIS 603. SEIS 610 can be taken concurrently with SEIS 601 or SEIS 603.
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40007
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 327
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40008
CoFlex:In Person&Online Sync | Lecture
St Paul: Owens Science Hall 275
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40585
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 230
Online
Modern IT infrastructure is evolving rapidly, driven by automation, scalability, and intelligence. This course explores Artificial Intelligence for IT Operations (AIOps) and Infrastructure as Code (IaC), two transformative practices shaping the future of IT systems. Students will learn how organizations design, deploy, and manage cloud scale infrastructure using automation and AI driven insights. Through hands on labs and real-world scenarios, the course covers key tools, patterns, and workflows for building resilient, scalable systems. By the end, students will be equipped to implement IaC for automated provisioning and apply AIOps techniques to optimize operations in dynamic environments. Prerequisite: SEIS 615
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40416
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40227
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40006
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 331
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40220
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 331
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40153
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 313
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40175
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 238
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40143
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 326
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40152
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40655
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 326
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40602
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40658
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40142
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall LL18
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40024
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 127
Online
This course addresses how organizations can create advantage by aligning Business Strategy, Enterprise Architecture, and Artificial Intelligence capabilities. Students learn how enterprise architecture provides the structure needed to drive strategy enablement through effective use of AI. The course examines the relationships among business strategy, the multiple layers of enterprise architecture, and AI strategy, including how AI can influence competitive positioning and operating models. Current industry trends such as digital transformation, cloud platforms, data governance, and responsible AI are integrated throughout. By the end of the course, students will be able to assess AI capabilities and design architectures and roadmaps that connect technical execution to strategic goals.
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40185
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 313
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. Prerequisite: SEIS 630
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40415
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 333
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40445
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 314
Online
A growing number of connected devices continuously stream data using familiar web protocols and patterns. In our increasingly digital world, this data is relied 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 603 and SEIS 630
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40446
CoFlex:In Person&Online Sync | Lecture
St Paul: Schoenecker Center 408
Online
A growing number of connected devices continuously stream data using familiar web protocols and patterns. In our increasingly digital world, this data is relied 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 603 and SEIS 630
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40379
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 325
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, Data Preparation and Analysis
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40657
Online: Sync Distributed | Online: Synchronous
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, Data Preparation and Analysis
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40228
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 325
Online
Artificial Intelligence develops computational models that emulate intelligent behavior through learning, perception, and decision-making. With the advent of deep learning, AI systems now achieve state-of-the-art performance in domains such as computer vision, natural language processing, and sequential pattern recognition. This course provides a rigorous introduction to the design and training of artificial neural networks, including techniques for improving generalization and adapting models to new tasks. Students will gain hands-on experience in implementing and optimizing deep learning architectures, while also examining the theoretical foundations that govern their behavior. Emphasis is placed on evaluating model performance, diagnosing training challenges, and understanding the limitations and risks associated with deploying AI systems in real-world contexts. Prerequisite: SEIS 763
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40659
Online: Sync Distributed | Lecture
Online
Artificial Intelligence develops computational models that emulate intelligent behavior through learning, perception, and decision-making. With the advent of deep learning, AI systems now achieve state-of-the-art performance in domains such as computer vision, natural language processing, and sequential pattern recognition. This course provides a rigorous introduction to the design and training of artificial neural networks, including techniques for improving generalization and adapting models to new tasks. Students will gain hands-on experience in implementing and optimizing deep learning architectures, while also examining the theoretical foundations that govern their behavior. Emphasis is placed on evaluating model performance, diagnosing training challenges, and understanding the limitations and risks associated with deploying AI systems in real-world contexts. Prerequisite: SEIS 763
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40587
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 325
Online
Modern AI applications are not just models. They are complex software systems that combine machine learning, data pipelines, retrieval systems, infrastructure, and experimentation. Building reliable AI applications requires careful system design, operational tooling, and measurement. This course teaches how to design and build production AI systems. Students learn the architectural patterns used in industry to deploy machine learning models, large language models, and retrieval systems at scale. Topics include model serving, message queues, vector databases, ranking systems, prompt management, experimentation, monitoring, and scalable infrastructure. Through lectures, architecture workshops, and hands-on labs, students develop a practical toolbox for building AI systems and learn how to apply these tools when designing real world applications. Prerequisite: SEIS 767. Students can either take SEIS 767 concurrently or before SEIS 765.
3 Credits
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40588
CoFlex:In Person&Online Sync | Lecture
St Paul: Owens Science Hall 251
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
| 09/09 - 12/21 | ||||||
| M | T | W | Th | F | Sa | Su |
5:30 pm 5:30 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 40589
CoFlex:In Person&Online Sync | Lecture
St Paul: O'Shaughnessy Science Hall 230
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