4T-Instructional Series in Machine Learning and Artificial Intelligence
This course provides a detailed introduction to Decision Trees, a powerful and versatile technique in the field of machine learning. Participants will explore key concepts, classification and regression techniques, as well as their practical application on real-world datasets. Additionally, the exciting topic of code generation using large language models will be addressed.
Course Structure:
Unit 1: Decision Trees for Classification
- Decision Trees Characteristics
- Gini Impurity Score, Entropy Inpurity Score
- Training Algorithm, Hyperparameters, Computational Complexity, Model Sensitivity and Stability
- First Decision Tree Classification Example
- Detailed Calculation of the Gini Score
- Decision Boundaries, Estimation of Class Probabilities, Making Predictions
- Detailed Calculation of the Entropy Score
- Second Decision Tree Classification Example
- Underfitting, Overfitting, Tree Depth
- Decision Tree Classification Example using the Iris Data Set
- Decision Regions, Confusion Matrix
- Feature Importance, Grid Search
Unit 2: Decision Trees for Regression
- Model Characteristics, Regression Tree Models, Training Algorithm
- Regression Tree Example, Overfitting versus Underfitting
- Regression Tree Example (continues)
- Overfitting
- Model Regularization
- Hyperparameter Optimization via GridSearcCV, Model Regularization via GridSearchCV
Unit 3: Decision Tree Example
- The California Housing Data Set
- Instantiate Regression Tree, Use GridSearchCV, Evaluate Performance, Visualize Tree
- Instantiate Random Forest Regressor, Use GridSearchCV, Evaluate Performance
- Instantiate Random Forest Regressor using Hyperparameters found through GridSearcCV, Evaluate Performance
- Decision Tree Overview (assigned reading)
Unit 4: Code Generation using Large Language Models
- Getting Started with the PaLM API (Google), Text and Code Generation
- A Possible Prompt Structure, Build your Prompt, Generate Code, Run the Generated Code
- A Second Code Generation Trial using a Higher Temperature, Run the Generated Code
- Getting Started with OpenAI API, Code Generation using DaVinci Model and GPT-4
Learning Objectives | Participants will explore key concepts, classification and regression techniques, as well as their practical application on real-world datasets. |
---|---|
Contact Hours | 4 Horas |
CIAPR courses | CURSO TECHNICO |
Instructor | Marvi Teixeira, PhD |
Devices | Desktop, Tablet, Mobile |
Language | Español |
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