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

Skills for module:

Python

Machine Learning

Artificial Intelligence

Scikit Learn

NumPy

Matplotlib

Jupyter Notebooks

Algorithms

Algebra

Problem Solving

Critical Thinking

Time Management

Data Science

Continuous Integration

Hyperparameters

Boosting

Neural Networks

Mathematics

Data Visualisation

Machine Learning

CS3920

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