Overview
In the Artificial Intelligence program, you’ll get a comprehensive overview of the theory and practice of artificial intelligence. The program is focused on modern computational techniques for representing task-relevant information and making intelligent decisions. As a participant, you’ll gain practical, real-world experience by considering the use and development of decision support systems for medical environments, recommender systems for supported ad targeting, and intelligent systems for autonomous work environments and processes. And because the problem-solving methods presented are applicable across a wide range of industrial, civil, medical, financial, robotic, and information systems, you’ll complete the program with the skills you need to help your organization stay ahead of the technology curve — and take your skills for applying AI-powered solutions to the next level.
Course Description & Learning Outcomes
In this program, you will:
Identify reasonable representations for given AI problems
Consider the use of basic symbolic and numeric techniques
Apply algorithms to representations when finding solutions to AI problems
Examine how AI is incorporated into various AI applications Evaluate the ethical and societal implications of the field of AI
Recommended 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.
Pre-course instructions
You may find out more about the course by downloading the brochure here: https://emrt.us/B2B_Brochure_CMU_INAI
You may also email [email protected] for any queries or to enquire about the enrolment process.
In order to qualify for the 20% corporate discount provided to Deep Tech Central members by registering through Deep Tech Central
Schedule
End Date: 20 May 2026, Wednesday
Location: Online
Agenda
| Day/Time | Agenda Activity/Description |
|---|---|
| Module 7 | Randomized Algorithms |
| Module 8 | Common AI Applications Models |
| Module 9 | Human–AI Interaction |
| Module 10 | Autonomous Agents |
| Module 1 | Search: Evaluate search as a technique to find a solution to an artificial intelligence (AI) problem with a set of goal criteria |
| Module 2 | State and Action Representation |
| Module 3 | Constraint Satisfaction |
| Module 4 | Probability and Markov Processes |
| Module 5 | Machine Learning Models |
| Module 6 | Practical Machine Learning |
Pricing
Course fees: USD2,500 before 20% discount for all Deep Tech Central members who sign up through the registration here In order to qualify for the 20% discount, sign up for the course on Deep Tech Central, using the registration link available here or by emailing us at [email protected]
Skills Covered
PROFICIENCY LEVEL GUIDE
Beginner: Introduce the subject matter without the need to have any prerequisites.
Proficient: Requires learners to have prior knowledge of the subject.
Expert: Involves advanced and more complex understanding of the subject.
- Algorithms (Proficiency level: Proficient)
- API Development (Proficiency level: Beginner)
- Systems Engineering (Proficiency level: Beginner)
- Basic Engineering Knowledge (Proficiency level: Proficient)
- Cloud Computing (Proficiency level: Proficient)
Speakers
Trainer's Profile:
Stephanie Rosenthal, Assistant Teaching Professor, Carnegie Mellon University, School of Computer Science, Carnegie Mellon University
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.
Trainer's Profile:
Reid Simmons, Research Professor and Director of the Bachelor Science in Artificial Intelligence (BSAI), Carnegie Mellon University, School of Computer Science, Carnegie Mellon University
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).
