MS in Machine Learning
E62360
MS in Machine Learning is a specialized graduate program at Carnegie Mellon University focused on advanced theory and applications of machine learning and statistical methods for building intelligent systems.
All labels observed (1)
| Label | Occurrences |
|---|---|
| MS in Machine Learning canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T500848 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MS in Machine Learning Context triple: [Machine Learning Department, Carnegie Mellon University, offersProgram, MS in Machine Learning]
-
A.
Master of Information and Data Science
The Master of Information and Data Science is a professional graduate degree program focused on advanced data science methods, analytics, and their real-world applications in industry and research.
-
B.
Machine Learning Department, Carnegie Mellon University
The Machine Learning Department at Carnegie Mellon University is a pioneering academic unit dedicated to research and education in machine learning, artificial intelligence, and related computational disciplines.
-
C.
Lifelong Learning Machines program
The Lifelong Learning Machines program is a DARPA research initiative aimed at developing AI systems that can continuously learn and adapt from experience in dynamic, real-world environments.
-
D.
MS in Robotic Systems Development
MS in Robotic Systems Development is a professional graduate program at Carnegie Mellon University’s Robotics Institute that focuses on the practical design, development, and commercialization of advanced robotic systems.
-
E.
Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MS in Machine Learning Target entity description: MS in Machine Learning is a specialized graduate program at Carnegie Mellon University focused on advanced theory and applications of machine learning and statistical methods for building intelligent systems.
-
A.
Master of Information and Data Science
The Master of Information and Data Science is a professional graduate degree program focused on advanced data science methods, analytics, and their real-world applications in industry and research.
-
B.
Machine Learning Department, Carnegie Mellon University
The Machine Learning Department at Carnegie Mellon University is a pioneering academic unit dedicated to research and education in machine learning, artificial intelligence, and related computational disciplines.
-
C.
Lifelong Learning Machines program
The Lifelong Learning Machines program is a DARPA research initiative aimed at developing AI systems that can continuously learn and adapt from experience in dynamic, real-world environments.
-
D.
MS in Robotic Systems Development
MS in Robotic Systems Development is a professional graduate program at Carnegie Mellon University’s Robotics Institute that focuses on the practical design, development, and commercialization of advanced robotic systems.
-
E.
Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
- F. None of above. chosen
Statements (41)
| Predicate | Object |
|---|---|
| instanceOf |
graduate degree program
ⓘ
master’s program ⓘ |
| country |
United States
ⓘ
linked to:
United States of America
|
| degreeLevel | master’s ⓘ |
| emphasizes |
building intelligent systems
ⓘ
rigorous mathematical foundations ⓘ statistical reasoning ⓘ |
| fieldOfStudy |
artificial intelligence
ⓘ
machine learning ⓘ statistics ⓘ |
| focusesOn |
advanced theory of machine learning
ⓘ
applications of machine learning ⓘ deep learning ⓘ optimization for machine learning ⓘ probabilistic modeling ⓘ reinforcement learning ⓘ statistical learning theory ⓘ statistical methods for intelligent systems ⓘ supervised learning ⓘ unsupervised learning ⓘ |
| hasComponent |
coursework
ⓘ
project-based learning ⓘ research opportunities ⓘ |
| hasReputationFor |
close ties to machine learning research at CMU
ⓘ
strong theoretical training in machine learning ⓘ |
| institutionType | private research university program ⓘ |
| languageOfInstruction | English ⓘ |
| location | Pittsburgh, Pennsylvania ⓘ |
| offeredBy |
Carnegie Mellon University
ⓘ
linked to:
CMU
School of Computer Science at Carnegie Mellon University ⓘ |
| offeredByDepartment | Machine Learning Department at Carnegie Mellon University ⓘ |
| preparesFor |
PhD studies in related fields
ⓘ
industry roles in machine learning ⓘ research careers in machine learning ⓘ |
| relatedTo |
MS in Artificial Intelligence
ⓘ
MS in Computer Science ⓘ |
| requiresBackgroundIn |
linear algebra
ⓘ
probability ⓘ programming ⓘ statistics ⓘ |
| targetAudience | students with strong quantitative background ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: MS in Machine Learning Description of subject: MS in Machine Learning is a specialized graduate program at Carnegie Mellon University focused on advanced theory and applications of machine learning and statistical methods for building intelligent systems.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.