The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events Specialist
Advance your skills up the technology curve to solve more complex problems using AI-driven solutions.
TBD
10 weeks, online
8-10 hours per week
Our participants tell us that taking this program together with their colleagues helps to share common language and accelerate impact.
We hope you find the same. Special pricing is available for groups.
The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events SpecialistBased on the information you provided, your team is eligible for a special discount, for Artificial Intelligence starting on TBD .
We’ve sent you an email with enrollment next steps. If you’re ready to enroll now, click the button below.
Have questions? Email us at group-enrollments@emeritus.org.Feeling the pinch from seismic disruptions across all lines of business, organizations are fast-tracking AI projects and are thirsty for solutions to their most pressing challenges. IDC forecasts the AI market will break the $500 billion mark by 2024. This signals that the race for AI-capable talent is on.
This program is designed to provide a comprehensive overview of AI in both theory and practice with a focus on modern computational techniques for representing task-relevant information and making intelligent decisions. As a participant, you’ll develop AI problem-solving skills that are applicable across a large range of information systems to help you and your organization stay competitive in this rapidly evolving landscape.
SOURCE: U.S. NEWS & WORLD REPORT
This 10-week online program is designed to provide software developers, data science professionals, and other professionals aiming to advance their technical AI skills, allowing them to solve more complex challenges and add more value to their organization.
Participants who complete the program will be prepared to:
Structured as an online program, content is shared via live and recorded faculty videos and office hours with learning facilitators.
Leverage search techniques as a really powerful AI tool to help you find a set of actions that would take you from a starting state to a goal state.
Learn to translate a problem from an English statement into something AI can understand as you construct state and action representations for complex search problems and describe how they affect search performance.
Formulate a problem as a set of constraints and an objective and apply relevant algorithms to find a solution efficiently. Identify when the objective is to solve for a set of values and apply strategies to accomplish this goal.
Consider that many aspects of the world are probabilistic and evaluate how to translate that in AI. Demonstrate familiarity with probabilistic approaches to AI and implement algorithms to solve probabilistic sequential decision problems.
Reflect on machine learning (ML) as a popular approach employed to help businesses understand their data. Evaluate ML as a technique for finding patterns in data and explore popular ML algorithms and their corresponding data requirements.
Consider the many nuances to training and evaluating ML algorithms and explore the various ideas to help train better ML models. Analyze tuning hyperparameters that affect model performance and evaluate models properly.
Recognize that real-world problems have state spaces that are infeasible to search systematically. Demonstrate the applicability of randomized search techniques and understand when such approaches are appropriate to use.
Evaluate information retrieval and other AI technology behind search engines, personal assistants, and other question answering systems. Analyze recommender systems as a common AI application utilized in movie or music selection or for items to purchase.
Appreciate that AI systems do not act in a vacuum; they are typically completing tasks for or with people. Consider common approaches to integrating human feedback into AI systems and review current trends and challenges in AI systems that interact with people.
Evaluate the emerging use of AI in systems that can operate autonomously. Reflect on how reliably achieving complex tasks in dynamic environments is difficult and requires the careful integration of numerous AI techniques.
Leverage search techniques as a really powerful AI tool to help you find a set of actions that would take you from a starting state to a goal state.
Consider the many nuances to training and evaluating ML algorithms and explore the various ideas to help train better ML models. Analyze tuning hyperparameters that affect model performance and evaluate models properly.
Learn to translate a problem from an English statement into something AI can understand as you construct state and action representations for complex search problems and describe how they affect search performance.
Recognize that real-world problems have state spaces that are infeasible to search systematically. Demonstrate the applicability of randomized search techniques and understand when such approaches are appropriate to use.
Formulate a problem as a set of constraints and an objective and apply relevant algorithms to find a solution efficiently. Identify when the objective is to solve for a set of values and apply strategies to accomplish this goal.
Evaluate information retrieval and other AI technology behind search engines, personal assistants, and other question answering systems. Analyze recommender systems as a common AI application utilized in movie or music selection or for items to purchase.
Consider that many aspects of the world are probabilistic and evaluate how to translate that in AI. Demonstrate familiarity with probabilistic approaches to AI and implement algorithms to solve probabilistic sequential decision problems.
Appreciate that AI systems do not act in a vacuum; they are typically completing tasks for or with people. Consider common approaches to integrating human feedback into AI systems and review current trends and challenges in AI systems that interact with people.
Reflect on machine learning (ML) as a popular approach employed to help businesses understand their data. Evaluate ML as a technique for finding patterns in data and explore popular ML algorithms and their corresponding data requirements.
Evaluate the emerging use of AI in systems that can operate autonomously. Reflect on how reliably achieving complex tasks in dynamic environments is difficult and requires the careful integration of numerous AI techniques.
Office Hours with Learning Facilitators
Knowledge Checks
Dedicated Program Support Team
Case Studies
Programming Assignments
Discussion Boards
Wikis/Crowdsourced Activities
Scenario and Problem-based Learning
Software Developers/Technology Professionals seeking to gain a foundational understanding of AI processes and apply them in their work.
Data Science Professionals looking to develop an understanding of modern computational techniques for developing AI-based solutions.
Other Professionals who want to develop decision support systems (such as medical support or recommender systems), plan and monitor AI systems, develop mixed initiatives, and bring humans into the AI loop.
PREREQUISITES: Functional knowledge of calculus, linear algebra, probability, and statistics is beneficial. Participants should also have a working-level knowledge of Python, algorithm design, and data structures.
Take a self-assessment test to verify your readiness for the math and Python content in this program.
Stephanie Rosenthal
Reid Simmons
Stephanie Rosenthal
Assistant Teaching Professor, Carnegie Mellon University School of Computer Science
Stephanie Rosenthal is an Assistant Teaching Professor at Carnegie Mellon University School of Computer Science. She has many years of experience in higher education, teaching robotics, artificial intelligence, and applied data analytics. Her practitioner experience includes industry and government, with organizations such as Microsoft, Intel, the Department of Defense, and a robotics startup. Her research on AI and human–computer interaction aim to improve the decision making and performance of intelligent systems. She holds a PhD in Computer Science from Carnegie Mellon University.
Reid Simmons
Research Professor and Director of the Bachelor Science in Artificial Intelligence (BSAI), Carnegie Mellon University, School of Computer Science
Reid Simmons is a Research Professor and Director of the BSAI at Carnegie Mellon University School of Computer Science. As head of the Reliable Autonomous Systems Lab, the research investigates developing reliable, highly autonomous systems (especially mobile robots) that operate in rich, uncertain environments. As well as being a research professor at CMU, he served as Program Director at the National Science Foundation, overseeing the National Robotics Initiative and Smart and Autonomous Systems programs. He has contributed to hundreds of publications related to AI and has several decades of research experience in the field. He holds a PhD in Artificial Intelligence from the Massachusetts Institute of Technology (MIT).
Throughout the program, you’ll have the opportunity to apply your understanding of the content by completing computational problems and evaluating AI applications and methods in the context of practical problems. Example exercises include:
Upon successful completion of the program, participants will receive a verified digital certificate of completion from Carnegie Mellon University School of Computer Science Executive Education. This is a training program and it is not eligible for academic credit.
Download BrochureYour digital certificate will be issued in your legal name and emailed to you at no additional cost, upon completion of the program, per the stipulated requirements. All certificate images are for illustrative purposes only and may be subject to change at the discretion of Carnegie Mellon University’s School of Computer Science Executive Education.
