Applications of Deep Neural Networks

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6:00P-9:00P

TBA

Nadella

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T81 INFO 558

Deep learning is a group of exciting new technologies for neural networks. It is now possible to create neural networks of much greater complexity through a combination of advanced training techniques and neural network architectural components. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), transformers, large language models (LLMs), and classic neural network structures. The focus is primarily on applying deep learning to problems, with some introduction to mathematical foundations. High-Performance Computing (HPC) aspects demonstrate how you can leverage deep learning on graphical processing units (GPUs). Students will use Python to implement deep learning using PyTorch and other libraries. It is unnecessary to know Python before this course; however, familiarity with at least one programming language is assumed. We deliver this course in a hybrid format, including classroom and online instruction.

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2:30P-5:20P

TBA

Heaton

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Desc:

This course will be hybrid. In-person meeting dates: TBD.

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	Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use.

T81 INFO 559

Applications of Generative AI and Large Language Models

This course covers the dynamic world of Generative Artificial Intelligence providing hands-on practical applications of Large Language Models (LLMs) and advanced text-to-image networks. Using Python as the primary tool, students will interact with OpenAI's models for both text and images. The course begins with a solid foundation in generative AI principles, moving swiftly into the utilization of LangChain for model-agnostic access and the management of prompts, indexes, chains, and agents. A significant focus is placed on the integration of the Retrieval-Augmented Generation (RAG) model with graph databases, unlocking new possibilities in AI applications. As the course progresses, students will delve into sophisticated image generation and augmentation techniques, including LoRA (Low-Rank Adaptation), and learn the art of fine-tuning generative neural networks for specific needs. The final part of the course is dedicated to mastering prompt engineering, a critical skill for optimizing the efficiency and creativity of AI outputs. Ideal for students, researchers, and professionals in computer science or related fields, this course offers a transformative learning experience where technology meets creativity, paving the way for innovative applications in the realm of Generative AI. Note: This course will require the purchase of up to $100 in OpenAI API credits to complete the course.

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6:00P-9:00P

TBA

Heaton

May 7 2025 6:00PM - 9:00PM

Desc:

This course will be hybrid. In-person meeting dates: TBD

	Actions:		Books		Syllabus
	Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use.

T81 INFO 563

IT Governance & Risk Management

Firms with superior IT governance designed to support the organization's strategy achieve better performance and higher profits than firms with poor (or no) governance. Just as corporate governance aims to ensure quality decisions about all corporate assets, IT governance links IT decisions with company objectives and monitors performance and accountability. We will start with developing an understanding of IT governance and go over the decision-rights and decision-making processes associated with it. We will study various enterprise operating models and strategies and see how they in turn determine the IT strategy and operating model. We will also review practices to provide business oversight and transparency into IT investments and go over how IT leaders can proactively partner with other business leaders to drive top-line growth and/or operational cost savings. We will also review how to classify initiatives into projects and programs and group them into portfolios. Furthermore, we will discuss different project implementation approaches, ranging from waterfall to agile methodologies. We will also cover how many IT departments are transitioning towards a more productized delivery model. Finally, we will also review technology selection processes, architectural governance, procurement of products and services, as well as service and vendor management. Throughout this process, we will be reviewing decision-making from the perspective of both leveraging business opportunities and managing risk. Risk management is an essential component of an IT leader's role. We will gain an understanding of the risk management process and use it to identify and manage some of the common risks that contemporary IT organizations face. This includes guarding against cyber threats, protecting data privacy, and managing vendor, business continuity, regulatory, project, and operational risks etc.

Leadership Seminar for Technology Professionals

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6:00P-9:00P

TBA

Pellegrini

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	Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use.

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6:00P-9:00P

TBA

Hampton

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	Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use.

T81 INFO 570

This seminar is designed to develop the leadership capacity of professionals working in the information technology and cybersecurity fields. Although domain expertise plays an important role in the success of a technology professional, it's when this expertise is integrated with the ability to lead people that transforms the merely competent into multi-dimensional force multipliers for the organization. In this course, students will participate in an immersive, seminar-based learning experience targeted toward professional and personal development on a range of essential leadership skills. Students will benefit from interaction with industry experts in the IT and cybersecurity fields and receive coaching support to achieve professional and personal goals. Each student will complete a series of self- and multi-rater assessments as well as a personal leadership development plan to gain insight and build competencies critical to effective leadership. Topics include creating a shared vision, strategy development, building and sustaining a healthy culture, essentials of finance and budgeting, driving results, energizing people for performance, innovation, emotional intelligence, navigating organizational politics, managing up, negotiations, stress resilience, talent coaching and development, effective communication, and time management.

The steeply decreasing costs of gathering, storing, and processing data have created a strong motivation for organizations to move toward "data-driven" approaches to problem solving. As such, data analytics continues to grow rapidly in importance across industry, government, and nonprofit organizations. This course seeks to equip students with a wide range of data analytics techniques that serve as the foundation for a broad range of applications, including descriptive, inferential, predictive, and prescriptive analytics. Students will learn the process of building a data model as well as a variety of analytics techniques and under what situations they are best employed. Through lectures and practical exercises, students will become familiar with the computational mathematics that underpin analytics; the elements of statistical modeling and machine learning; model interpretation and assessment; and structured and unstructured data analysis. Students will also undertake a project to build an analytical model using a "real-world" dataset.

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6:00P-9:00P

TBA

Jaffe

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Desc:

This course requires professional work experience in technology. There is a $110 course fee placed on students' accounts for the required DISC Assessment (Non-Refundable once registered online). Students are responsible for purchasing the Harvard Business Course Packet for $88.14; see syllabus and instructors for more information.

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	Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use.

T81 INFO 574

Foundations of Analytics

Enterprise Data Management

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6:00P-9:00P

TBA

Greene

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	Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use.

T81 INFO 575

In the 4th Industrial Revolution of Digital Transformation, Data is a key and necessary foundational element. Enterprise Data Management is the responsibility and opportunity to effectively utilize data and make it useful to achieve organizational goals. Organizations have begun generating, collecting, and accumulating more data at a faster pace than ever before. The advent of "Big Data" has proven to be both an opportunity and challenge for organizations who are awash, even drowning-in data, but starved for knowledge. Unfortunately, many organizations have not developed comprehensive enterprise data management (EDM) practices that treat data as a true organizational asset. EDM is a comprehensive approach to defining, governing, securing, and maintaining the access & quality of the right data involved in the business processes of an organization. This course will cover many aspects of building an enterprise data management program, including areas such as data governance, data security, data architecture, data quality, data ownership, metadata management, data strategy, and others.

This course builds on the content taught in Enterprise Data Management and Foundations of Data Analytics. It focuses on the strategic, operational, tactical, and practical use of data analytics to inform decisions within an organization across a range of industry and government sector as well as within organizational functions. Students will be introduced to specific analytics techniques that are used currently by practitioners in areas of diagnostic, descriptive, predictive, and prescriptive analytics. Students will learn the critical phases of analytics including data preparation, model development, evaluation, validation, selection, and deployment. In so doing, students will learn to apply data analytics in order to optimize organizational processes, improve performance, and inform decision-making.

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2:30P-5:20P

TBA

McLean

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	Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use.

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6:00P-9:00P

TBA

McLean

Paper/Project/Take Home

	Actions:		Books		Syllabus
	Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use.

T81 INFO 576

Analytics Applications

The capstone course is the culmination of the Masters of Information Systems Management program. The capstone project provides the opportunity for students to employ the knowledge and skills they have gained from their coursework in a rigorous and systematic manner. Projects are sponsored by external corporate, government, and non-profit organizations and provide the opportunity to deliver meaningful research and recommendations for "real-world" IT challenges and problems.

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6:00P-9:00P

TBA

Lehmuth

Paper/Project/Take Home

	Actions:		Books		Syllabus
	Syllabi are provided to students to support their course planning; refer to the syllabus for constraints on use.