The Fitzpatrick Center at Duke University

AI Product Innovation Courses

The novel curriculum of the Duke AI for Product Innovation program prepares students with a well-rounded skillset to build and commercialize AI-based products.

Pre-Program Preparation

AIPI 503: Python Programming & Data Science Math Boot Camp

This six-week online bootcamp is designed to help students build proficiency in the use of Python for Data Science. It covers foundational concepts and it also provides hands-on experience with critical skills including loading, cleaning, manipulating, visualizing, analyzing and interpreting data. Upon completion of this course, students will be in a position to use their skills to identify, formulate and solve some practical data analysis problems.

The first two modules will provide an introduction to basic Python.  From there, the class will focus on building proficiency with three critical for Data Science Python libraries (Numpy, Pandas, Matplotlib).

The final module will focus on a review of probability and statistics with an emphasis on simulation of chance experiments. Topics for this last module will include discrete distributions, continuous density functions, combinatorics, conditional probabilities and a final review of important densities and functions.

Technical Core

AIPI 510: Sourcing Data for Analytics

In industry, one of the main activities, and challenges, of implementing machine learning applications are collecting data to use in modeling. This course introduces students to both the technical and non-technical (business, regulatory, ethical) aspects of collecting, cleaning, and preparing data for use in machine learning applications. The first segment of the course will be an introduction to numerical programming focused on building skills in working with data via the Numpy and Pandas libraries, two of the most common tools used by teams working with data and modeling. Technical aspects covered will include the types of data, methods of sourcing data via the web, APIs, and from domain-specific sensors and hardware (IoT devices), an increasingly common source of analytics data in technical industries. The course also introduces methods and tools for evaluating the quality of data, performing basic exploratory data analysis, and pre-processing data for use in analytics. Non-technical aspects covered include an introduction to data privacy, GDPR, regulatory issues, bias, and industry-specific concerns regarding data usage.

AIPI 520: Modeling Process & Algorithms

This course is an introduction to the modeling process and best practices in model creation, interpretation, validation, and selection of models for different uses. The primary machine learning algorithms, both supervised and unsupervised, are introduced and explained with the necessary level of mathematical theory to establish students’ intuition for how each algorithm works. The primary focus will be on “traditional” machine learning approaches but it will also introduce deep learning and its applications. At the end of this course, students should have a solid understanding of the end-to-end modeling process and the different types of model algorithms along with the strengths, weaknesses, assumptions, and use cases for each type.

AIPI 530: AI in Practice

AI in Practice will cover advanced topics in AI and will consist of two modules focused on 1) Optimization and 2) Reinforcement Learning.  The Optimization module will introduce students to optimization theory and programming and will include case studies on the practical applications of optimization to solve industry problems.  The Reinforcement Learning module will introduce RL and Deep RL theory and programming and will also include an emphasis on applications of reinforcement learning in industry.  

AIPI 540: Building Products Using Deep Learning

This course builds on the previous semester's core courses by bringing together the various aspects of a machine learning-based product implementation, including opportunity identification, data sourcing, business planning, and model prototyping and evaluation. The course is intended to be hands-on and includes a significant team project prototyping the use of a deep learning model within a new or existing industry product/service to create an additional value-add for the product. In addition to project-based learning, a number of industry-focused case examples will be used to strengthen each student's understanding of the applications for deep learning, including natural language processing, computer vision, and analysis of structured and time-series data. The course will reinforce students’ skills across all aspects of the product development process using AI and will build a solid understanding of the applications of deep learning in particular.

AI Operations Core

AIPI 501: Industry Seminar Series

Students will attend a weekly seminar series featuring industry leaders discussing the opportunities, challenges and learnings they have gained from applying AI to products and services in their industry. Speakers will present live in-classroom or via video conference.  The emphasis in the selection of speakers will be placed on representing more traditional industries that are poised to be disrupted by AI such as agriculture, health care/biotech, energy and environment, and manufacturing.  The seminar series will reinforce the concepts learned in the core courses and expand students’ intuition for the opportunities to apply AI within more complex and/or traditional industries.

AIPI 560: Legal, Societal & Ethical Implications of AI

Deploying AI within products and services has implications well beyond the technical considerations, which often include change management of operational workflows or staffing levels, data privacy considerations, bias risks and other ethical implications, and industry-specific regulations on the use of data and models operationally.  This course will introduce students to the key areas of consideration when deploying products that contain AI:

  1. Legal implications and industry regulation
  2. Ethical considerations
  3. Change management and organizational/societal implications

Case studies will be used extensively to provide real-world examples.

AIPI 561: Operationalizing AI

Deploying AI in production requires consideration of factors such as online model training, scaling, integration with software/hardware products, monitoring/support, security and failure resiliency.  This course introduces students via readings and real-world case studies to methods and best practices in deploying AI operationally within products and services, including both technology and support infrastructure considerations. The course will also introduce, although not go into deep technical detail on, the available technologies for working with Big Data in certain industries which require specialized infrastructure and tools due to the volume of data.

Management Core

MENG 540: Management of High-Tech Industries

Decision making in complex environments; emphasis on project analysis, complex investment analyses, strategic decision making where outcomes depend on high technology, and the role of decisions in product development. Management in high tech firms; emphasis on management of professionals, management of project-based and team-based organizational structures, and the role of the manager in expertise-driven organizations.

MENG 570: Business Fundamentals for Engineers

This comprehensive course examines core and evolving concepts in the business fundamentals of successful technology-based companies including Business Plan Development & Strategies, Marketing, Product & Process Development processes, Intellectual Property, Accounting, Finance, and Operations. Students will learn the fundamentals essential to understanding all aspects of a business and will be able to converse in some depth in each of the areas studied upon completion. Other topics will include Supply Chain Management, Stage-Gate Development Cycles, Balances Scorecards, Blue Ocean Strategy, and Disruptive Technologies.

Electives

Note: Students in this master's degree program may take additional electives, with program director approval, through Duke's Master of Engineering Management Program or other courses within Duke's Pratt School of Engineering. 

Technical Track

AIPI 590: Data Analysis at Scale in the Cloud

This course is designed to give students a comprehensive view of cloud computing including Big Data and Machine Learning. A variety of learning resources will be used including interactive labs on Cloud Platforms (Google, AWS, Azure). This is a project-based course with extensive hands-on assignments.

 

AIPI 570: AI Applications in Health Care & Biotech

This course will introduce students to the opportunities and the specific challenges associated with the deployment of AI within products and services in the healthcare and biotech industries.  The course assumes foundational knowledge in the concepts and programmatic implementation of machine learning and will focus on its application to specific areas of the industry, including computational phenotyping, predictive analytics, disease diagnosis via medical imaging, and personalized medicine via patient similarity.  Both traditional machine learning and deep learning techniques will be included, and assignments will combine conceptual understanding with programming implementation on real-world datasets. The course will introduce, but not extensively cover, Big Data technologies including Hadoop and Spark in the context of their usefulness for certain applications in the field.

 

AIPI 571: AI Applications in Energy & Infrastructure

This course will introduce students to the opportunities and the specific challenges associated with the deployment of AI within products and services in the energy and environment sectors.  Infrastructure managers, including transportation, water, power, and agriculture, collect increasingly vast amounts of data from users as well as sensors and hardware.  With the rapidly evolving power of AI, organizations in the energy and environment fields are beginning to realize the value that analytics can create for them in better managing their network, infrastructure or operations.  The course assumes foundational knowledge in the concepts and programmatic implementation of machine learning and will focus on its application to specific areas of the industry, such as agricultural analytics via deep learning, power/water usage and anomaly detection, and traffic prediction.

 

CYBERSEC 511: Cybersecurity in the Software Development Life Cycle

Teaches students about all steps of the software development lifecycle and the role Cybersecurity design principles play in requirements definition, design, development, testing, and maintenance. Tools and techniques to provide software security analysis and software assurance will be explored including software reverse engineering.

 

CYBERSEC 520: Applying Machine Learning to Advance Cybersecurity

The use of machine learning and AI is becoming more prevalent for collecting and analyzing data as its consolidation increases in value. Cyberattacks seek to steal, deny access, misrepresent (such as deepfakes), or compromise the privacy of information. Students will explore the power of machine learning and AI’s use in enhancing Cybersecurity tools across the NIST Framework and also in detecting and exploiting vulnerabilities in timeframes and ways heretofore unthinkable.

 

Technology Management Track

AIPI 570: AI Applications in Health Care & Biotech

This course will introduce students to the opportunities and the specific challenges associated with the deployment of AI within products and services in the healthcare and biotech industries.  The course assumes foundational knowledge in the concepts and programmatic implementation of machine learning and will focus on its application to specific areas of the industry, including computational phenotyping, predictive analytics, disease diagnosis via medical imaging, and personalized medicine via patient similarity.  Both traditional machine learning and deep learning techniques will be included, and assignments will combine conceptual understanding with programming implementation on real-world datasets. The course will introduce, but not extensively cover, Big Data technologies including Hadoop and Spark in the context of their usefulness for certain applications in the field.

 

AIPI 571: AI Applications in Energy & Infrastructure

This course will introduce students to the opportunities and the specific challenges associated with the deployment of AI within products and services in the energy and environment sectors.  Infrastructure managers, including transportation, water, power, and agriculture, collect increasingly vast amounts of data from users as well as sensors and hardware.  With the rapidly evolving power of AI, organizations in the energy and environment fields are beginning to realize the value that analytics can create for them in better managing their network, infrastructure or operations.  The course assumes foundational knowledge in the concepts and programmatic implementation of machine learning and will focus on its application to specific areas of the industry, such as agricultural analytics via deep learning, power/water usage and anomaly detection, and traffic prediction.

 

CYBERSEC 521: Cybersecurity Program Development, Operations & Analysis

Students will examine the life cycle of a cybersecurity program from development, administration, evaluation, and improvement processes. Operational and strategic roles including the chief information security officer (CISO) in a representative security team will be studied and current and evolving areas where the team is placed in the enterprise. Best practices and models for how a security team’s performance can be measured will be learned with some real-world examples.

 

 

EGRMGMT 560: Project Management

Projects are one of the key mechanisms for achieving organizational goals and implementing change, whether it is the design and launch of a new product, the construction of a new building, or the development of a new information system. This course will focus on defining project scope, developing project plans, managing project execution, validating project performance and ensuring project control. Additional topics covered include decision making, project finance, project portfolio selection, and risk management

 

EGRMGMT 575: Software Quality Management

This class will introduce students to five different business personas that play a key role in the software development lifecycle—these are customer, software engineer, software release/quality manager, customer support engineer, and general manager. The students will better appreciate the perspectives that each of these personas brings to their role and how that affects the "delivered" quality that customers actually experience. The course will also help students understand how to assess customer business outcomes, expectations and measure customer experience. Finally, the class will provide exposure to current industry practices and include guest speakers who can give real-world examples relevant to software quality management

 

EGRMGMT 576: Design Thinking and Innovation

The success of established companies and entrepreneurial ventures depends upon their ability to identify customer needs and then develop products and services that meet these needs in an affordable and effective manner. A disciplined design thinking process leads to successful innovations, particularly with regard to value creation and market impact. Starting with an understanding of empathy, ethnography, and interviewing techniques, moving on to the iterative process of defining, ideating, prototyping, and testing, and then developing final designs, this course is a highly engaging opportunity for students to develop a deep set of skills in design thinking and innovation and includes current approaches such as agile development, biodesign, and lean startup.

 

 

Capstone Project

AIPI 449-550: AI Product Innovation Capstone Project

Students will work in teams of 3-5 to complete a two-semester ML/AI project for a company sponsor around a real-world problem or opportunity they are facing. The project will require students to put into practice many of the skills learned during the program, including both technical and non-technical.  The capstone project experience begins in the Spring semester and continues full-time during the Summer semester until the end of July. Students will have the opportunity to engage directly with representatives from their company partner organization as well as receive guidance from Duke faculty members over the course of the project.