(*)- Implementing Classification and Regression Models (k3)
Students can practically implement classification and regression models using various supervised learning techniques, understanding how to fit models to data and make predictions.
- Evaluating Model Performance Using Metrics (k4)
Students are able to compute and interpret performance metrics, such as confusion matrices and ROC curves, to assess the quality and reliability of machine learning models.
- Applying Cross-Validation and Hyperparameter Tuning (k4)
Students can perform cross-validation and optimize hyperparameters to enhance model robustness and prevent overfitting, ensuring better generalization on new data.
- Experimenting with Advanced Supervised Learning Methods (k4)
Students are capable of applying algorithms like logistic regression, support vector machines (SVMs), and neural networks, exploring their performance and limitations in different problem contexts.
- Analyzing Time Series Data and Sequence Prediction (k4)
Students can handle sequence data and apply machine learning techniques to analyze and predict time series patterns, understanding their structure and temporal dependencies.
- Implementing Ensemble Methods and Feature Engineering (k4)
Students can use ensemble methods such as bagging and boosting to improve model performance and apply feature selection and construction techniques to refine input data for models.
- Balancing Bias and Variance in Model Training (k5)
Students are able to identify and address issues of under- and overfitting, effectively balancing bias and variance to optimize model performance.
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(*)Students have practical knowledge of implementing and evaluating supervised learning models, including concepts of classification, regression, and model validation. They know how to apply advanced techniques like SVMs, neural networks, ensemble methods, and feature engineering while understanding how to address model complexity and performance challenges through cross-validation and hyperparameter tuning.
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