The Online Graduate Certificate from Penn Engineering Online is a for-credit credential that will produce an academic transcript and paper certificate. To earn a certificate, students can take a maximum of four (4) course units. Two of these four course units may be double-counted from your Penn Engineering graduate degree program (except for the Health Care and Technology certificate, wherein only one course, HCIN 6022, may double-count).
Students may earn a maximum of two certificates. No course may be triple counted, i.e., counted for more than two credentials.
While most individuals will complete the Online Graduate Certificate program within one year, students may choose to extend their studies. In this case, all Certificate requirements must be met within a maximum of two years.
*Note: Degree students will receive first priority for course registration.
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
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
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.
This hands-on capstone course in the MSE in AI Online program provides students with the opportunity to design, develop, and deploy AI-driven applications that serve as portfolio-ready demonstrations of their skills. Students will apply techniques and concepts learned throughout the program to tackle open-ended, practical problems, projects may be team-based.
Students will identify a meaningful AI application idea, scope the project, and define deliverables. Projects may include – but are not limited to – LLM-based applications, computer vision systems, reasoning agents, AI for scientific discovery, or multi-modal AI experiences. Students are encouraged to select projects that integrate multiple AI techniques and reflect the full development cycle, from ideation to deployment. As part of this cycle, students will be expected to work with open source software and models, continue training or fine-tune models for their application, and robustly evaluate their systems using existing benchmarks or custom-built evaluation metrics that they create as part of the capstone.
Weekly office hours and check-ins will provide a forum to discuss technical and design challenges, review progress, and share feedback on implementation and user experience. Instructor and TAs will guide students in best practices for system design, model selection and fine-tuning, data management, evaluation, and ethical considerations in AI deployment.
The course will culminate in a public-facing website showcasing the student projects, and a final presentation where students present and demo their applications to peers and faculty. These portfolio demos will be polished and professional, making them suitable to share with potential employers as evidence of applied AI expertise.
Pre-Requisites
Required: CIS 5210, CIS 5300, and either ESE 5410 or ESE 5420. 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. Please note, as a course expense, students will need to subscribe to an AI service for the duration of Capstone. The cost is estimated to be approximately $50/month.
