The MSE-TI degree consists of 10 course units (CUs), with all courses being fully online and no real-time sessions required.
Students complete four core courses in innovation and professional practice, three courses within a selected concentration area (Artificial Intelligence, Data Science, or Software Systems), and three additional elective courses that provide technical breadth or allow preparation for advanced study in related fields. Coursework for the MSE-TI based on concentration can be accessed using the tabs below.
Students who wish to pursue coursework spanning two Advanced Technical Concentrations may do so by using their free elective courses, provided all degree and prerequisite requirements are met. Such plans should be developed in consultation with an academic advisor to ensure appropriate depth and coherence. Courses may be applied toward either core or one Advanced Technical Concentration and may not be double-counted to satisfy core and multiple concentration requirements simultaneously.
Note: MSE-TI Online students are waived from needing to complete CIT 5910, CIT 5920, CIT 5930, & CIT 5940 as pre-req requirements.
Technology Innovation & Entrepreneurship introduces students to the process of building high-tech ventures from idea to launch. Designed for engineers and scientists with an interest in innovation, the course develops the knowledge and skills needed to recognize entrepreneurial opportunities, shape them into viable business models, and communicate them effectively. Students work through a 14-module sequence, each aligned with a key component of a professional pitch deck. Along the way, they gain hands-on experience in opportunity analysis, business planning, and financial modeling. The course emphasizes clear communication and evidence-based decision making, culminating in a comprehensive pitch presented for peer and expert review.
Engineering economics provides a systematic framework for evaluating the economic aspects of competing design or project alternatives. Topics include: cost-driven design economics, break-even analysis, money-time relationships and equivalent worth, rates of return, cost estimation, pricing strategy, depreciation and taxes, inflation, foreign currency exchange rates, life cycle analysis, benefit/cost ratio analysis, replacement analysis, dealing with uncertainty, probabilistic risk analysis, capital financing and cost of capital, and financial statement analysis. Case studies apply these topics to real-world problems.
Intellectual Property Strategy and Business Law for Engineers
Announcing the first iPhone at Macworld 2007, Apple CEO Steve Jobs famously boasted: “And boy, have we patented it!” How, and to what extent, does intellectual property actually provide competitive advantage for innovative technology companies? What makes an IP asset strategically powerful? How do patents impact – and sometimes drive – major corporate decisions including M&A, venture funding and exits, and entry into new markets?
Designed especially for engineering students with entrepreneurial goals, strong interest in technology innovation is expected, but there is no need for deep expertise in any particular technical area to excel in this course. Students who aspire to be technology developers, executives advisers and/or investors in innovation-driven industries will find this course valuable as well.
The course is divided into three units:
-In Unit 1, Patents and Protecting Valuable Innovations, we learn how to analyze the scope of protection provided by patents, and we examine closely how and when that form of protection translates to competitive advantage. We practice applying these concepts to critique and improve the patents that protect a company’s most important innovations.
-In Unit 2, IP Leverage and the Corporate Playbook, we learn transaction basics, and study theory and examples of how IP leverage strategically informs a variety of corporate transactions. We will examine the “contextual” value of patents, for established companies as well as for start-ups, and will analyze the benefits and pitfalls of various IP strategies.
-In Unit 3, Beyond Patents: The Data/AI Revolution, we confront important, recent legal changes reigning in the scope and power of patents. We analyze the business impact of these sea changes in light of the dramatic rise of AI-powered innovation, where non-patent IP assets such as data create new, distinctive opportunities and risks.
Students who take and succeed in this course should expect to acquire insights and methods that they can utilize throughout their careers to contribute important value as stakeholders in innovative technology businesses, from emerging start-ups to industry leaders.
This course introduces engineering students to human-centered design approaches for exploring and solving complex problems. Through an individual project, students learn foundational design methods—research, empathy-building, problem framing, ideation, prototyping, and iteration—and build complementary skills in systems thinking, early experimentation, and clearly communicating design decisions.
Students will strengthen their creative thinking abilities, practice using visualization and rapid prototyping to test ideas, and reflect on their evolving design process as engineers. By the end of the course, they will have created and tested a prototype and gained an introductory understanding of how design and engineering come together in contemporary innovation practice.
Engineers design and build the world we live in. From algorithms to bridges, cars to drones, every day we entrust our safety and prosperity to the decisions that engineers make. It is unsurprising that ethics is part of the foundation on which the modern engineering profession is built. But sometimes ethical engineering decisions nonetheless harm us more than they help us. Where this happens, engineers may face legal liability. Such liability is a legal question, not an engineering question. This course introduces students both to traditional concepts of engineering ethics as well as to the legal and policy background against which the ethics of engineering decisions are ultimately evaluated. Particular attention is paid to questions that arise in the context of new technologies such as artificial intelligence; case studies involving artificial intelligence and similar technology are considered throughout.
This course is organized in three units. It begins with a broad consideration of the role of engineers and ethics in society and the role that the law plays in formalizing a society’s ethical intuitions, primarily focusing on US legal institutions and their philosophical underpinnings. It then considers the different ways that engineers and the law understand risk and how those differing understandings of risk affect product design and professional liability. It concludes by surveying legal topics of particular interest to engineering professions such as intellectual property, privacy and security, regulation, and antitrust. Contemporary challenges, such as ethical issues posed by artificial intelligence and the challenges of regulating firms with significant market power, are considered throughout.
Students enrolled in this class will be asked to read a range of materials, including excerpts from legal memos and judicial opinions, philosophical texts, and engineering studies. Assessments will include regular short writing assignments.
In the new era of big data, we are increasingly faced with the challenges of processing vast volumes of data. Given the limits of individual machines (compute power, memory, bandwidth), increasingly the solution is to process the data in parallel on many machines. This course focuses on the fundamentals of scaling computation to handle common data analytics tasks. You will learn about basic tasks in collecting, wrangling, and structuring data; programming models for performing certain kinds of computation in a scalable way across many compute nodes; common approaches to converting algorithms to such programming models; standard toolkits for data analysis consisting of a wide variety of primitives; and popular distributed frameworks for analytics tasks such as filtering, graph analysis, clustering, and classification.
Pre-Requisites
CIT 5910 Introduction to Software Development or equivalent programming experience; Broad familiarity with probability and statistics, as well as programming in Python; Additional background in statistics, data analysis (e.g., in Matlab or R), and machine learning is helpful (example: ESE 5420 Statistics for Data Science: An Applied Machine Learning Course)
Structured information is the lifeblood of commerce, government, and science today. This course provides an introduction to the broad field of information management systems, covering a range of topics relating to structured data, from data modeling to logical foundations and popular languages, to system implementations. We will study the relational data model; SQL; database design using the Entity-Relationship model and relational design theory; transactions and updates; efficient storage of data; indexes; query execution and query optimization; and “big data” and NoSQL systems.
Pre-Requisites
CIT 5910 Introduction to Software Development, CIT 5920 Mathematical Foundations of Computer Science | Knowledge of Javascript & Web Development (HTML, CSS) is recommended. | Recommended Corequisite: CIT 5960 Algorithms & Computation
Machine Learning for Data Science is a foundational course designed to equip students with the essential skills necessary for a career in data science and machine learning. This comprehensive course delves into the fundamentals of machine learning, addressing key concepts such as the curse of dimensionality, model selection and validation, regularization, bootstrap and uncertainty quantification. Students will gain hands-on experience with a variety of machine learning models including regression and classification trees, ensemble learning, boosting, support vector machines, neural networks, hierarchical clustering and K-means. The curriculum is structured to provide practical Python programming skills, which are crucial for succeeding in subsequent courses. By applying these techniques to real-world scenarios in finance, business and industry, the course ensures that students not only understand the theory behind machine learning but also how to apply it effectively in professional settings. This course is an indispensable part of the educational journey for aspiring data scientists, laying the groundwork for further studies and applications in the field.
Pre-Requisites
CIT 5920 Mathematical Foundations of Computer Science, Programming background, Basic Probability
The course covers the methodological foundations of data science, emphasizing basic concepts in statistics and learning theory, but also modern methodologies. Learning of distributions and their parameters. Testing of multiple hypotheses. Linear and nonlinear regression and prediction. Classification. Uncertainty quantification. Model validation. Clustering. Dimensionality reduction. Probably approximately correct (PAC) learning. Such theoretical concepts are further complemented by exemplar applications, case studies (datasets), and programming exercises (in Python) drawn from electrical engineering, computer science, the life sciences, finance, and social networks.
Pre-Requisites
CIT 5920 Mathematical Foundations of Computer Science, Programming background, Basic Probability
Offerings
Data Science Technical Elective – 1 Course Unit
This requirement may be fulfilled by selecting one additional course from the Data Science core course listed above or from the list of technical electives below.
This 1.0 credit unit course focuses on algorithmic techniques needed for processing large amounts of data. A traditional course in algorithm design teaches foundational principles (divide-and-conquer, dynamic programming, graph algorithms) that are primarily suited for solving computational problems in polynomial time. What happens when traditional techniques meet modern scales, and “polynomial”-time is too slow? This course will set the mathematical and algorithmic foundations for processing large amounts of data. The key will be to incorporate randomness and approximation and to develop the algorithmic techniques needed to keep up with ever-increasing scales.
The successful student will leave the class with a theoretical computer science perspective on problems and models for massive data. They will gain knowledge of various algorithmic techniques and reasoning tools to design algorithms using randomness and approximation. They will furthermore learn the fundamental limits of these techniques. This aligns with the Computer Science program objective to produce graduates with the ability to successfully apply analytical, and problem-solving skills, and to reason about computational approaches for algorithmic problems with modern scales.
Pre-Requisites
CIT 5920; Suggested: An undergraduate algorithms course (like CIS 1210 or equivalent course), or comfort with models of computation, asymptotic complexity, and basic probability.
This course investigates algorithms to implement resource-limited knowledge-based agents which sense and act in the world. Topics include: search, machine learning, probabilistic reasoning, natural language processing, knowledge representation and logic. After a brief introduction to the language, programming assignments will be in Python. MSE-AI students must take this course in their first semester.
This course provides an overview of the field of natural language processing. The goal of the field is to build technologies that will allow machines to understand human languages. Applications include machine translation, automatic summarization, question answering systems, and dialog systems. NLP is used in technologies like Amazon Alexa and Google Translate.
Pre-Requisites
CIT 5910 Introduction to Software Development, CIT 5920 Mathematical Foundations of Computer Science, and CIT 5940 Data Structures & Software Design. Recommended: CIT 5960
In this course, we will explore massively parallel programming, specifically on graphics processing units (GPUs), with immediate application to machine learning (ML) and artificial intelligence (AI). We’ll first outline computational aspects of ML and connect parallel programming to common components of deep learning. You will gain proficiency in GPU programming basics through hands-on projects with industry best practices and tools, eventually building up to implementing components of modern neural models.
After completing the course, you will have knowledge of:
– parallel programming concepts (hardware, software, and networks) working together to accelerate performance;
– modern distributed ML computation in a GPU datacenter setting as it relates to large-scale neural networks;
– machine and deep learning workloads from a computational perspective to build more efficient systems;
– using tools like profilers and debuggers to accelerate performance and solve programming challenges.
Please note: This course is designed with the expectation that students will need to spend up to $300 on cloud computing resources in addition to tuition. Any expenses beyond this amount are also the responsibility of the student.
REQUIRED: CIT 5930, CIT 5940, CIT 5950, CIT 5960, Intermediate/advanced knowledge of C/C++, Intermediate knowledge of Linear Algebra and Calculus (in particular, differentiation). RECOMMENDED: ESE 5460, CIS 5530, Familiarity with Unix/Linux. NOTE: MSE-DS Online students are waived from needing to complete CIT 5910, CIT 5920, CIT 5930, & CIT 5940 as pre-req requirements. MSE-AI Online students are waived from needing to complete CIT 5910, CIT 5920, CIT 5930, CIT 5940, CIT 5950, and CIT 5960 as pre-req requirements.
This is an introductory course to computer vision and computational photography. This course will explore four topics: 1) image feature detection, 2) image morphing, 3) image stitching, and 4) deep learning related to images. This course is intended to provide a hands-on experience with interesting things to do on images/pixels. The world is becoming image-centric. Cameras are now found everywhere: in our cell phones, automobiles, and even in medical surgery tools. In addition, computer vision technology has led to innovations in areas such as movie production, medical diagnosis, biometrics, and digital library. This course is suited for students with any engineering background who have a basic understanding of linear algebra and programming, along with plenty of imagination.
Pre-Requisites
CIT 5910 Introduction to Software Development, CIT 5920 Mathematical Foundations of Computer Science, CIT 5930 Introduction to Computer Systems and CIT 5940 Data Structures & Software Design. Students may take CIT 5950 Computer Systems Programming and/or CIT 5960 Algorithms & Computation concurrently with this elective.
This course in the Data Science Program provides students an opportunity to work on an end-to-end real-world data science project by leveraging students’ existing industry partners. Students will work with their Capstone mentors and the course instruction team to identify a data science problem, apply knowledge from previous courses to design a solution and learn new skills and techniques to implement their proposed solution.
Weekly instructor office hours will be used to discuss questions around the components of the data science project lifecycle, consider common issues with projects, brainstorm ideas for addressing stumbling blocks, and seek and share feedback on project decisions and progress. The student will be guided jointly by the course instructor and by a Capstone mentor selected by the student in the area of the project.
Upon completing the course, students are expected to have gained essential skills to tackle real-world problems through a data science perspective. This course is specifically designed for students who have already identified a semester-long project that covers all aspects of the data science pipeline and have secured an industry mentor. We strongly advise you to enroll only if you have these two aspects arranged in advance. If for some reason you have not yet found a mentor, please contact your Academic Advisor via pennengonline@engineering.upenn.edu before the course begins.
Required: CIS 5450, ESE 5410, and CIS 5500. Recommended: Students should be nearing the end of their program and/or should have the required background for their proposed project in order to register. Please note that registration for this course requires program approval.
This 0.5 CU course is an excellent introduction for those who want to learn about the mechanics of data, performing data analysis to gain insights, applying data science techniques to make predictions, and applying data analytics to answer questions and to address interesting business problems. Students will learn how to interpret and frame business problems to be addressed by analytics. The course will also cover different elements of the data analytics process, including data wrangling and cleaning, data exploration and descriptive analytics, data modeling, machine learning, predictive analytics, data visualization and the presentation of analysis and insights using data storytelling. While we will touch upon essential theoretical and technical concepts, our primary focus in this course will be on the practical application of data skills.
This 0.5 CU course provides a comprehensive introduction to the field of imaging informatics, with a focus on radiology as the clinical imaging domain. Students will learn about the importance of informatics to the clinical practice of radiology, the unique types of data encountered, relevant data and transactional standards, the growing role of artificial intelligence in radiology, and the challenges faced by imaging informaticists around the globe. This course is geared to any student interested in imaging informatics, and does not require prior training or experience in medicine or medical imaging. Homework assignments include synthesizing reading content and preparing written responses, managing radiology data though coding, and using generative AI to explore health literacy. Unlike other offerings in the course catalog, Imaging Informatics provides a distinctive blend of informatics and radiology, focusing on practical applications and hands-on experience in managing and interpreting medical imaging data, with less focus on intensive coding and technical skill development.
This 0.5 CU course provides a comprehensive introduction to medical image analysis. Students will learn the basics of Computer Vision with an emphasis on the special challenges of automated medical image analysis for clinical healthcare and medical research. Students will be required to visually assess the images, and work with key Machine Learning technology to interpret data on the actual medical image scans. The course is appropriate for students without prior medical or imaging training.
Learn to integrate generative AI models with Unreal Engine to streamline content creation for film, gaming, and virtual production. This practical course teaches you to use AI as a tool for environment design, asset generation, and procedural content creation within professional development pipelines. No previous game development experience is necessary.
You’ll explore AI-assisted workflows that help both large production teams and independent developers iterate quickly on concepts and fill content gaps. The course balances hands-on technical implementation with critical analysis of where these tools excel and where human creativity remains essential.
Key topics include:
Integration techniques for AI models within UE production pipelines
AI-assisted asset creation strategies
Evaluation of AI capabilities, limitations, and ethical considerations in media production
Industry workflows for AI-enhanced content development
Throughout the course, you will engage in application-based projects to reinforce your understanding:
Build AI-enhanced virtual environments using Unreal Engine
Create a portfolio piece demonstrating technical and creative applications
Please Note: This course requires students to cover minimal external costs for specialized third-party software licensing necessary to complete the core assignments. These fees are expected to be less than $100 and will be confirmed prior to the start of the course.
Pre-Requisites
CIS 5210. Basic background in neural networks and Python programming.
Deep networks are at the heart of modern approaches in computer vision, natural language processing and robotics. Design of these networks requires a combination of intuition, theoretical foundation and empirical experience; this course discusses general principles of deep learning that cut across these three. It develops insight into popular empirical practices with a focus on the training of deep networks, builds theoretical skills to develop new ideas in deep learning and to deploy deep networks in real world applications. A fair degree of mathematical and programming proficiency is necessary to complete the coursework.
EAS 5160 and EAS 5170 together, or ESE 5420, or CIS 5150. Recommended: Students should be halfway complete with their program of study in order to register.
Engineers design and build the world we live in. From algorithms to bridges, cars to drones, every day we entrust our safety and prosperity to the decisions that engineers make. It is unsurprising that ethics is part of the foundation on which the modern engineering profession is built. But sometimes ethical engineering decisions nonetheless harm us more than they help us. Where this happens, engineers may face legal liability. Such liability is a legal question, not an engineering question. This course introduces students both to traditional concepts of engineering ethics as well as to the legal and policy background against which the ethics of engineering decisions are ultimately evaluated. Particular attention is paid to questions that arise in the context of new technologies such as artificial intelligence; case studies involving artificial intelligence and similar technology are considered throughout.
This course is organized in three units. It begins with a broad consideration of the role of engineers and ethics in society and the role that the law plays in formalizing a society’s ethical intuitions, primarily focusing on US legal institutions and their philosophical underpinnings. It then considers the different ways that engineers and the law understand risk and how those differing understandings of risk affect product design and professional liability. It concludes by surveying legal topics of particular interest to engineering professions such as intellectual property, privacy and security, regulation, and antitrust. Contemporary challenges, such as ethical issues posed by artificial intelligence and the challenges of regulating firms with significant market power, are considered throughout.
Students enrolled in this class will be asked to read a range of materials, including excerpts from legal memos and judicial opinions, philosophical texts, and engineering studies. Assessments will include regular short writing assignments.
Any online CIT, CIS, ESE, EAS, or ENGR courses at the 5000 level or higher may be chosen as free electives. These electives are intended to allow students to fill in knowledge gaps before taking technical electives or to gain technical breadth in areas outside their primary specialization cluster.
