Machine Learning

Learning Outcomes:

• Understanding the fundamental concepts of Machine Learning
• Applying the Nearest Neighbours algorithm in Machine Learning problems
• Gaining introductory knowledge of conformal prediction and its significance in Machine Learning for reliable predictions
• Completing the discussion on full conformal prediction and its applications in Machine Learning
• Understanding the risks of overfitting and underfitting in Machine Learning models
• Learning about learning curves and their importance in evaluating Machine Learning models
• Discussing the method of Least Squares and its advancements, like Ridge Regression and Lasso
• Exploring the impact of data preprocessing and parameter selection on the quality of Machine Learning predictions
• Studying inductive conformal prediction and its computational efficiency
• Applying kernel methods to add flexibility to linear Machine Learning models
• Understanding the concepts and applications of neural networks and support vector machines
• Learning to use pipelines in Machine Learning workflows with scikit-learn
• Studying cross-conformal predictors and their efficiency
• Gaining a broad understanding of various prediction algorithms in Machine Learning