Subject: Software Eng (Grad) (SEIS)

CRN: 40058

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 333

Online

  Michael Dorin

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40496

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 333

Online

  Gaurav Shrestha

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40237

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 326

Online

  Michael Dorin

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40238

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 327

Online

  Syed Naqvi

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40239

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 326

Online

  Syed Naqvi

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40057

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 333

Online

  Michael Dorin

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40011

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 408

Online

  Ron Chiang

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40012

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 331

Online

  Ron Chiang

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40679

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 408

Online

  Ron Chiang

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40459

Online: Sync Distributed | Lecture

Online

  Gaurav Shrestha

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40250

Online: Sync Distributed | Lecture

Online

  Sanjay Mathur

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40010

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 331

Online

Requirements Met:
     Software Data Mgmt Conc
     Software Technical Elective

  Abe Kazemzadeh

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40240

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 331

Online

Requirements Met:
     Software Data Mgmt Conc
     Software Technical Elective

  Abe Kazemzadeh

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40168

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 230

Online

  John Chandler

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40190

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 313

Online

  John Chandler

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40158

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 227

Online

Requirements Met:
     LLM/MSL Elective

  Paul Kaefer

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40167

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 313

Online

Requirements Met:
     LLM/MSL Elective

  Jessi Benzel

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40896

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 327

Online

Requirements Met:
     LLM/MSL Elective

  Paul Kaefer

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40798

Online: Sync Distributed | Lecture

Online

  Brandan Keaveny, Jessi Benzel

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40899

Online: Sync Distributed | Lecture

Online

  Brandan Keaveny, Jessi Benzel

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40157

CoFlex:In Person&Online Sync | Lecture

St Paul: Owens Science Hall 275

Online

  Melinda Mattox

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40036

Online: Sync Distributed | Lecture

Online

  Sanjay Mathur

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40090

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 327

Online

Requirements Met:
     Software Data Mgmt Conc
     Software Technical Elective

  Nate Crawford

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40200

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 327

Online

Requirements Met:
     Software Data Mgmt Conc
     Software Technical Elective

  Nate Crawford

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40458

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 333

Online

  Michael Dorin

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

Subject: Software Eng (Grad) (SEIS)

CRN: 42813

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 230

Online

  Justin Grammens

This course is designed for students to be exposed to technologies and best practices that help them understand both the high-level concepts at a systems level and the supporting technologies that make up the combination of Machine Learning and the Internet of Things. TinyML, short for Tiny Machine Learning is a fast-growing field of Machine Learning technologies that are able to run on-device sensor data analytics using extremely low power. Improvements in optimization algorithms and frameworks for running inferences at the edge, it is now possible to make IoT devices smarter. Students will get to build a rapid prototype of a real product and put it into practice to collect and analyze data to make predictions. The course will provide a foundation on capturing data from the physical world and applying Machine Learning techniques to gain predictions and insights at the edge. Prerequisites: SEIS 601 or SEIS 603 or an equivalent understanding of foundational programming concepts.

3 Credits

Subject: Software Eng (Grad) (SEIS)

CRN: 40494

CoFlex:In Person&Online Sync | Lecture

St Paul: Owens Science Hall 251

Online

  Cort Lunke

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40495

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 328

Online

  Emily Helland

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40419

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 325

Online

  Chih Lai

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40898

CoFlex:In Person&Online Sync | Lecture

St Paul: Owens Science Hall 257

Online

  Shrey Pareek

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40251

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 325

Online

  Chih Lai

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40900

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 313

Online

  Manjeet Rege

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40681

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 325

Online

  Jim Howard

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40682

CoFlex:In Person&Online Sync | Lecture

St Paul: O'Shaughnessy Science Hall 325

Online

  Chih Lai

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

Subject: Software Eng (Grad) (SEIS)

CRN: 40683

CoFlex:In Person&Online Sync | Lecture

St Paul: Schoenecker Center 408

Online

  Abe Kazemzadeh

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