https://github.com/lmizner/ece219_datamining
Course work from UCLA's ECE219 - Large-Scale Data Mining
https://github.com/lmizner/ece219_datamining
bayesian-graphical-models causal-modelling deep-learning kernel-techniques large-scale-dataset machine-learning predictive-modeling regression-models regularization supervised-learning support-vector-machines unsupervised-learning
Last synced: 3 months ago
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Course work from UCLA's ECE219 - Large-Scale Data Mining
- Host: GitHub
- URL: https://github.com/lmizner/ece219_datamining
- Owner: lmizner
- Created: 2024-06-24T19:44:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-18T16:22:09.000Z (7 months ago)
- Last Synced: 2025-02-08T07:41:37.427Z (5 months ago)
- Topics: bayesian-graphical-models, causal-modelling, deep-learning, kernel-techniques, large-scale-dataset, machine-learning, predictive-modeling, regression-models, regularization, supervised-learning, support-vector-machines, unsupervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 36.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
### Large-Scale Data Mining: Models and Algorithms
Introduction of variety of scalable data modeling tools, both predictive and causal, from different disciplines. Topics include supervised and unsupervised data modeling tools from machine learning, such as support vector machines, different regression engines, different types of regularization and kernel techniques, deep learning, and Bayesian graphical models. Emphasis on techniques to evaluate relative performance of different methods and their applicability. Includes computer projects that explore entire data analysis and modeling cycle: collecting and cleaning large-scale data, deriving predictive and causal models, and evaluating performance of different models.