University / Personal Experiments2023Completed
Anomaly Detection Using Gaussian Model
Gaussian anomaly detection with density modeling, threshold tuning, and F1 evaluation.
- Role
- ML Experimentation
- Work track
- University / Personal Experiments
- Category
- Applied AI Systems
Context and problem
Anomaly detection needed a simple probabilistic baseline with measurable threshold selection.
Execution and contribution
Implemented univariate and multivariate Gaussian modeling, validation-set threshold tuning, and F1-based evaluation.
Outcomes and proof
Recorded results and supplied evidence from the work.
- 01
- Gaussian model
- 02
- Threshold tuning
- 03
- F1 evaluation
- Built a statistical anomaly workflow.
- Compared false-positive and detection behavior.
- Documented threshold selection logic.
- Applied ML workflow design with evaluation-first optimization under operational constraints.
Available evidence
- Anomaly detection evaluation slides
- Instruction manual result figures
Gallery and evidence
Clinical ML Pipeline and supplied project material.
Technical record
Stack
- MATLAB
- Gaussian Model
- Thresholding
Focus
- Anomaly Detection
- Statistical ML
- Experiment
Resources
Related work
More records from the same work track or technical category.
