Algorithms and Data Structures for Machine Learning (3 Credits)
University of Maryland, College Park · MD · MSML606
Algorithms and Data Structures for Machine Learning (3 Credits) is a subject at University of Maryland, College Park, MD. It covers topics like algorithms, computational-mathematics, data-structures, machine-learning. It is part of 6 degrees. The information below is compiled from public course material — approximate, not official.
University of Maryland, College Park
MSML606
3.00
Topics covered
Degrees that include Algorithms and Data Structures for Machine Learning (3 Credits)
- Applied Machine Learning (online) (SAMO)
- Applied Machine Learning (SAML)
- Artificial Intelligence (MSAI)
- Artificial Intelligence (online) (MSAO)
- Machine Learning (MPML)
- Machine Learning (online) (MPMO)
Frequently asked questions
What is Algorithms and Data Structures for Machine Learning (3 Credits) about?
Algorithms and Data Structures for Machine Learning (3 Credits) covers topics like algorithms, computational-mathematics, data-structures, machine-learning.
Which degrees include Algorithms and Data Structures for Machine Learning (3 Credits)?
Algorithms and Data Structures for Machine Learning (3 Credits) is part of 6 degrees, including Applied Machine Learning (online) (SAMO), Applied Machine Learning (SAML), Artificial Intelligence (MSAI), Artificial Intelligence (online) (MSAO), Machine Learning (MPML).
How many credit points is Algorithms and Data Structures for Machine Learning (3 Credits)?
Algorithms and Data Structures for Machine Learning (3 Credits) at University of Maryland, College Park is typically around 3.00 credit points. Approximate — confirm with the university.
Where is Algorithms and Data Structures for Machine Learning (3 Credits) taught?
Algorithms and Data Structures for Machine Learning (3 Credits) is taught at University of Maryland, College Park, MD.
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Sources: University of Maryland, College Park public course catalogue. Information is compiled and summarised — not copied verbatim — and is approximate; always confirm with the original source.
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