Data Science

# DS 4400: Machine Learning and Data Mining 1

Lecture - 4 credits

ND

EI

IC

FQ

SI

AD

DD

ER

WF

WD

WI

EX

CE

- Introduces supervised and unsupervised predictive modeling, data mining, and machine-learning concepts.
- Uses tools and libraries to analyze data sets, build predictive models, and evaluate the fit of the models.
- Covers common learning algorithms, including dimensionality reduction, classification, principal-component analysis, k-NN, k-means clustering, gradient descent, regression, logistic regression, regularization, 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.
- Requires programming in R and Python.
- Applies concepts to common problem domains, including recommendation systems, fraud detection, or advertising.

Introduces supervised and unsupervised predictive modeling, data mining, and machine-learning concepts.

*Show more.*Pre-requisites