The Fitzpatrick Center at Duke University

Course Descriptions

Our novel master's curriculum was developed with industry input and structured around the core activities of the Product Development Process. More »

Click for details

Pre-Program Preparation

Python Programming & Data Science Math Boot Camp

This course 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.

Duration

  • Full-time students: 2.5 weeks
  • Part-time and certificate students: Spread over 6 weeks

Industry Preparation 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.

Technical Core

AIPI 510: Sourcing Data for Analytics

In industry, one of the main activities, and challenges, of implementing machine learning applications is 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: Applying AI in Practice

This course focuses on the implementation via programming of the data pre-processing and machine learning modeling process, taught in a case-based format where students gain experience in applying the concepts learned in the other core courses (Sourcing Data for Analytics, and Modeling Process & Algorithms) on real-world scenarios in industrial domains. The course will be taught in Python, as the language used today by the vast majority of teams in industry working with data modeling. The content will consist of case studies drawing on real-world, often messy datasets that will reinforce students’ programming skills in cleaning, exploring and visualizing data, applying machine learning algorithms, and interpreting and validating results in the context of solving business problems. Programming assignments will expose students to several of the commonly used existing tools and libraries for implementing ML.

AIPI 540: Building Products Using Deep Learning

This course builds on the previous semester 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.

Electives

Course numbers to come.

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.

Prerequisites

  • Sourcing Data for Analytics
  • Modeling Process & Algorithms
  • Applying AI in Practice

Pre-/co-requisite

  • Building Products Using Deep Learning

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.

Prerequisites

  • Sourcing Data for Analytics
  • Modeling Process & Algorithms
  • Applying AI in Practice

Pre-/co-requisite

  • Building Products Using Deep Learning

AI Applications in Industrial Products & Systems

This course will introduce students to the opportunities and the specific challenges associated with the deployment of AI within the manufacturing sector.  AI offers the potential to reduce errors, significantly improve production times, and boost safety.  The course assumes foundational knowledge in the concepts and programmatic implementation of machine learning and will focus on its application to specific areas of manufacturing including anomaly detection, automation, and digital twins.  Course content will focus on industry examples, and assignments will take the form of case studies to ensure an applications-focus.

Prerequisites

  • Sourcing Data for Analytics
  • Modeling Process & Algorithms
  • Applying AI in Practice

Pre-/co-requisite

  • Building Products using Deep Learning.

Professional Development

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.

Capstone Project

AIPI 449-550: AI For Product Innovation Capstone Project

Students will work in teams of 3-5 to complete a two-semester ML/AI project for an industrial company sponsor around a real-world problem or opportunity they are facing. The project will require students to put in practice many of the skills learned during the program, including both technical and non-technical.  While students will manage their own projects directly with the client sponsors, Duke faculty will oversee the process and provide significant learning opportunities throughout the experience.

Prerequisites

  • Sourcing Data for Analytics
  • Modeling Process & Algorithms
  • Applying ML in Practice
  • Product Applications of Deep Learning