Detailed Course Information

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DS 4400 - Machine Learning
Introduces supervised and unsupervised predictions, modeling, and essential machine learning concepts. Uses tools and libraries to analyze datasets, build predictive models, and evaluate the fit of the models. Covers common learning algorithms such as regression; classification (including support vector machines and logistic regression); dimensionality reduction (including principal-component analysis); clustering, gradient descent, regularization techniques, multiclass data, and algorithms; boosting; and decision trees. Studies computational aspects of probability, statistics, and linear algebra that support algorithms, including sampling theory and computational learning. Applies concepts to common problem domains. 4.000 Credit hours 4.000 Lecture hours Levels: Undergraduate Schedule Types: Lecture Data Science Department Course Attributes: NUpath Analyzing/Using Data, NUpath Capstone Experience, NUpath Writing Intensive, Computer&Info Sci Restrictions: Must be enrolled in one of the following Levels: Undergraduate Prerequisites: Undergraduate level DS 3000 Minimum Grade of D- or Undergraduate level CIS 250M Minimum Grade of D- or (Undergraduate level MATH 2331 Minimum Grade of D- and Undergraduate level MATH 3081 Minimum Grade of D-)

DS 4400 - Machine Learning

Introduces supervised and unsupervised predictions, modeling, and essential machine learning concepts. Uses tools and libraries to analyze datasets, build predictive models, and evaluate the fit of the models. Covers common learning algorithms such as regression; classification (including support vector machines and logistic regression); dimensionality reduction (including principal-component analysis); clustering, gradient descent, regularization techniques, multiclass data, and algorithms; boosting; and decision trees. Studies computational aspects of probability, statistics, and linear algebra that support algorithms, including sampling theory and computational learning. Applies concepts to common problem domains.
4.000 Credit hours
4.000 Lecture hours

Levels: Undergraduate
Schedule Types: Lecture

Data Science Department

Course Attributes:
NUpath Analyzing/Using Data, NUpath Capstone Experience, NUpath Writing Intensive, Computer&Info Sci

Restrictions:
Must be enrolled in one of the following Levels:
Undergraduate

Prerequisites:
Undergraduate level DS 3000 Minimum Grade of D- or Undergraduate level CIS 250M Minimum Grade of D- or (Undergraduate level MATH 2331 Minimum Grade of D- and Undergraduate level MATH 3081 Minimum Grade of D-)

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Detailed Course Information

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