CSCI 345 Slides
| Title | Description |
|---|---|
| Lecture 4 - 1: Classification Metrics | confusion metrics, precision, recall, f1-score, PR curves, AP score, ROC curve, ROC AUC, class imbalance |
| Lecture 4 - 2: Regression Metrics | Regression metrics |
| Lecture 4 - 3: Evaluating Models with Plots | Evaluating models with plots |
| Lecture 5: Model Validation | Model validation, cross-validation, and hyperparameter tuning in scikit-learn. |
| Lecture 6: Model Improvement | Pre-processing, Transformations, and pipelines. |
| Lecture 6 -2 : Model Selection and Evaluation | Pre-processing, Transformations, and pipelines. |
| Lecture 7: Decision Trees | Decision trees for classification & regression tasks, Decision tree algorithms |
| Lecture 7: Decision Trees | Understanding Overfitting |
| Lecture 8: Ensemble Models | Bootstrapping, bagging, random forests, and feature importance. |
| Lecture 9: ML Model Deployment | A practical guide to taking ML models from notebooks to production. |
| Neural Networks from Zero | |
| Support Vector Classifiers |
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