Enrollment and waitlist data for current and upcoming courses refresh every 10 minutes; all other information as of 6:00 AM.
| 09/07 - 12/19 | ||||||
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5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 40811
Online: Sync Distributed | Lecture
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
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41602
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 326
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
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41603
Online: Sync Distributed | Lecture
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
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41604
Online: Sync Distributed | Lecture
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
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 40810
Online: Sync Distributed | Lecture
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
| M | T | W | Th | F | Sa | Su |
09/10: 09/24: 10/08: 10/22: 11/05: 11/19: 12/03: |
Subject: Software Eng (Grad) (SEIS)
CRN: 41049
In Person | Lecture
St Paul: Owens Science Hall 250
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
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 40133
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 325
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. Prerequisite: SEIS 610, students can take SEIS 610 concurrently
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 40134
Online: Sync Distributed | Lecture
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. Prerequisite: SEIS 610, students can take SEIS 610 concurrently
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 40135
Online: Sync Distributed | Lecture
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. Prerequisite: SEIS 610, students can take SEIS 610 concurrently
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 43956
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. Prerequisite: SEIS 601 or SEIS 603
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41679
Online: Sync Distributed | Lecture
Online
In the competitive technology market space, all organizations are working hard towards retaining and adding new customers. In light of this objective, organizations continue to evolve in finding new ways to best manage and deliver their high quality software products to customers on time and within budget. SEIS-627 provides an introduction to different work management practices in software development. Topics covered in this course include traditional software development practices prescribed by PMI PMBOK as well as product management focusing on agile delivery practices. This course also includes hands-on projects to help students simulate real-world experiences as Project and Product Managers. Prerequisite: SEIS 610 AND SEIS 601/603
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 40132
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 313
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. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41607
Online: Sync Distributed | Lecture
Online
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. Prerequisite: SEIS 610. SEIS 630 may be taken concurrently with SEIS610.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41419
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 326
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 601 or SEIS 603 (may be taken concurrently).
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41469
Online: Sync Distributed | Lecture
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 601 or SEIS 603 (may be taken concurrently).
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41396
Online: Sync Distributed | Lecture
Online
Requirements Met:
LLM/MSL Elective
The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41417
Online: Sync Distributed | Lecture
Online
Requirements Met:
LLM/MSL Elective
The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41472
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 329
Requirements Met:
LLM/MSL Elective
The course provides an introduction to concepts and techniques used in field of data analytics and visualization. Data analytics is defined to be the science of examining raw data with the purpose of discovering knowledge by analyzing current and historical facts. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery, and best practices for creating effective data visualizations. Through practical application of the above topics, students will also develop proficiency in using analytics tools.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41395
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 326
This 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), security regulations, and disaster recovery. We will explore social engineering and other human factors and the impact to security. There will be an emphasis on local area networking (LAN) and Internet architecture and protocols, including TCP/IP and the OSI layers. We study protocol details, the way they relate and interact with each other, and how they are applied in real systems. Prerequisite: SEIS610
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41493
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 327
This broad survey course covers IT and digital delivery, operations, and management in both theory and practice. Topics include IT and digital value; digital infrastructure including cloud; Agile and Lean influences including DevOps; product and service management; work management; operations management, coordination including process management; IT investment and portfolio; organization and cultural factors; IT management frameworks; IT governance, risk, security, compliance; enterprise information management; and enterprise architecture. Class sessions emphasize hands-on, team-based learning. Introductory Linux command-line skills are covered. Prerequisite: SEIS 610
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 43149
Online: Sync Distributed | Lecture
Online
Digital Transformation is everywhere: business to business, business to consumer and even government to citizens. Digital transformation promises a bridge to a digital future, where organizations can thrive more fluid business models and processes. In this course, we start by showing the step by step of what digital transformation is, harnessing various exponential technologies and the five domains of digital transformation: Customers, Competition, Data, Innovation, and Value. A deep dive into data, the economic value of data, and data monetization in a B2B and B2C context. Understanding the layers of data, value proposition and business models play a holistic and practical guide for a digital-first organization and professional to transform legacy businesses or create new value propositions in the digital age. We also take an in-depth look at many technologies, including data science, that are part of many successful digital transformations.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 40376
Online: Sync Distributed | Lecture
Online
This course provides students with a theoretical and practical understanding of Strategy and Enterprise Architecture (EA). It studies how EA enables organizations to effectively accomplish their business goals. Specifically, the course analyzes the relationships among business strategies, IT strategies, business, applications, information, and technology architectures. It also examines current industry trends such as: design thinking, digital transformation, cloud migration, and introduces students to EA implementation frameworks and tools.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 40462
Online: Sync Distributed | Lecture
Online
This course will examine the confluence of technologies that underpin blockchain-based distributed ledgers that first appeared in cryptocurrencies like Bitcoin.New terminology is introduced, followed by discussions regarding why this technology is disruptively powerful and a philosophical inquiry into the nature of money itself.The course breaks down the role of “mining” and demonstrates why the economics of the current implementations are not scalable (or even profitable). The process of building blocks one technology at a time from the underlying revision control system, the communication channel known as “gossip,” to achieving consensus in both a trusted and untrusted world will be covered.Students will examine practical case studies beyond cryptocurrencies, which will include critical identification of when these technologies are not practical. Finally, the course will conclude with an in-depth exploration into Smart Documents and Smart Contracts and their possible outcomes.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41050
Online: Sync Distributed | Lecture
Online
In order to build and maintain a successful data warehouse and business intelligence environment that delivers real world business value, it is important to understand all of the components and how they fit together. This course will cover data warehouse and data mart lifecycle phases as well as business intelligence approaches while focusing on architecture, infrastructure, design, implementation and management issues. The course project will provide an opportunity for hands-on experience with some of the available business intelligence, data warehousing tools and technologies. Topics include: differences between data warehouses and traditional database systems (OLTP), data modeling, planning for data warehouses, extraction transformation and loading (ETL), data governance and data quality, common pitfalls to avoid when designing, implementing and maintaining data warehouse environments, organizing data for analysis, and the impact of new technologies (data streaming, data lakes, cloud data warehouses, etc.). Prerequisite: SEIS630
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41497
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 327
In order to build and maintain a successful data warehouse and business intelligence environment that delivers real world business value, it is important to understand all of the components and how they fit together. This course will cover data warehouse and data mart lifecycle phases as well as business intelligence approaches while focusing on architecture, infrastructure, design, implementation and management issues. The course project will provide an opportunity for hands-on experience with some of the available business intelligence, data warehousing tools and technologies. Topics include: differences between data warehouses and traditional database systems (OLTP), data modeling, planning for data warehouses, extraction transformation and loading (ETL), data governance and data quality, common pitfalls to avoid when designing, implementing and maintaining data warehouse environments, organizing data for analysis, and the impact of new technologies (data streaming, data lakes, cloud data warehouses, etc.). Prerequisite: SEIS630
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 40410
Online: Sync Distributed | Lecture
Online
The healthcare data is inherently heterogeneous with numeric health records, semi-structural medical text, and medical images. This course will discuss how to apply the latest artificial intelligence approaches in analyzing different types of healthcare data. Real-world projects to be discussed in this course include (1) training artificial intelligence models to learn patterns from 16-million medical papers and doctors’ notes for predicting potential disease outcomes, (2) analyzing patient health records to detect frequent medical sequences for treatment and prevention (3) applying machine vision methods in analyzing fish embryo images for identifying morphological changes due to toxic chemical exposure, (4) using deep-learning methods to analyze motions in telemedicine videos, (5) building clinic decision support systems to detect possible prescription errors, (6) querying databases on National Library of Medicine to enhance medical decisions, (7) imputing medical data with up to 95% missing values. Prerequisites: SEIS 639 or SEIS 764
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41297
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 328
As data is becoming more and more ubiquitous, the need to consume it to perform computations and power intelligent systems is also becoming more important. Bigger and more powerful neural networks need a large amount of data to be more accurate in performing tasks and making decisions. This means that it is increasingly important to understand the architecture and data plumbing for such sophisticated systems of the future. This course provides a broad coverage of the building blocks of a modern big data architecture which is fast, scalable and reliable. Major topics covered in this course include: (1) persistent storage and data organization (2) data ingestion and integration, (3) batch and stream processing, (4) modern cloud architectures, and (5) a real life example of geospatial analytics using such architecture. Students will complete hands on exercises leveraging big data tools to build data pipelines. Prerequisites: (SEIS 601 or SEIS 603) and SEIS 630. May take concurrently with SEIS 737.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41416
Online: Sync Distributed | Lecture
Online
As data is becoming more and more ubiquitous, the need to consume it to perform computations and power intelligent systems is also becoming more important. Bigger and more powerful neural networks need a large amount of data to be more accurate in performing tasks and making decisions. This means that it is increasingly important to understand the architecture and data plumbing for such sophisticated systems of the future. This course provides a broad coverage of the building blocks of a modern big data architecture which is fast, scalable and reliable. Major topics covered in this course include: (1) persistent storage and data organization (2) data ingestion and integration, (3) batch and stream processing, (4) modern cloud architectures, and (5) a real life example of geospatial analytics using such architecture. Students will complete hands on exercises leveraging big data tools to build data pipelines. Prerequisites: (SEIS 601 or SEIS 603) and SEIS 630. May take concurrently with SEIS 737.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 41397
Online: Sync Distributed | Lecture
Online
This course covers the technical concepts of managing vast amount of unstructured, semi-structured and structured data, collectively called "Big Data". Due to the sheer volume of Big Data, traditional approaches to managing databases does not work well for Big data and does not perform as expected. A distributed architecture for both the file system and the operating system is needed. Some of the techniques used in managing Big Data have the origins in the research and the developments that have been going on for decades in the area of parallel processing and distributed database management systems. This course focuses on why big data sets must be distributed and the issues that distribution introduces. The basic concepts on which distributed data sets are handled are discussed first. Once a foundation is defined, software tools that we use to work with big data sets are studied to provide an in-depth analysis of the concepts introduced. Specifically, we will study the issues distributed data design, data fragmentation, data replication, distributed fault tolerance/recovery. We will use various tools in dealing with big data sets and use real life examples of how these open source software are used. Prerequisites:(SEIS 601 or SEIS 603) and SEIS 630. May take concurrently with SEIS 736.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41520
In Person | Lecture
St Paul: Binz Refectory LL02
This course covers the technical concepts of managing vast amount of unstructured, semi-structured and structured data, collectively called "Big Data". Due to the sheer volume of Big Data, traditional approaches to managing databases does not work well for Big data and does not perform as expected. A distributed architecture for both the file system and the operating system is needed. Some of the techniques used in managing Big Data have the origins in the research and the developments that have been going on for decades in the area of parallel processing and distributed database management systems. This course focuses on why big data sets must be distributed and the issues that distribution introduces. The basic concepts on which distributed data sets are handled are discussed first. Once a foundation is defined, software tools that we use to work with big data sets are studied to provide an in-depth analysis of the concepts introduced. Specifically, we will study the issues distributed data design, data fragmentation, data replication, distributed fault tolerance/recovery. We will use various tools in dealing with big data sets and use real life examples of how these open source software are used. Prerequisites:(SEIS 601 or SEIS 603) and SEIS 630. May take concurrently with SEIS 736.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 43955
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 326
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 601 and SEIS 610.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41494
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 325
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
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 43153
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 325
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
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
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Subject: Software Eng (Grad) (SEIS)
CRN: 41609
Online: Sync Distributed | Lecture
Online
Machine Learning builds computational systems that learn from and adapt to the data presented to them. It has become one of the essential pillars in information technology today and provides a basis for several applications we use daily in diverse domains such as engineering, medicine, finance, and commerce. This course covers widely used supervised and unsupervised machine learning algorithms used in industry in technical depth, discussing both the theoretical underpinnings of machine learning techniques and providing hands-on experience in implementing them. Additionally, students will also learn to evaluate effectiveness and avoid common pitfalls in applying machine learning to a given problem. Prerequisites: SEIS 631 and 632, 632 can be taken concurrently.
3 Credits
| 09/07 - 12/19 | ||||||
| M | T | W | Th | F | Sa | Su |
5:45 pm |
||||||
Subject: Software Eng (Grad) (SEIS)
CRN: 41682
In Person | Lecture
St Paul: O'Shaughnessy Science Hall 327
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
| M | T | W | Th | F | Sa | Su |
09/10: 09/24: 10/08: 10/22: 11/05: 11/19: 12/03: |
Subject: Software Eng (Grad) (SEIS)
CRN: 41683
Online: Sync Distributed | Lecture
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