In the coming term, Prof. Douglas Leith and I will be teaching an introduction to machine learning here at Trinity College Dublin, Ireland. We expect around 200 students, primarily from the new Master of Science in Intelligent System and Data Science. Many students will already have a background in machine learning, and I am looking forward to teaching about the following topics relating to machine learning:

  1. Machine Learning Basics (Applications of Machine Learning, Challenges in Machine Learning, Alternatives to Machine Learning)
  2. Machine Learning in Action (Machine-Learning Datasets, Machine-Learning Frameworks, Machine-Learning Evaluation)
  3. Cross-validation and confidence intervals for Machine-Learning
  4. Overfitting/underfitting in Machine-Learning (bias-variance trade-off)
  5. Machine Learning Algorithms
    • Linear Regression
    • Logistic Regression
    • Support Vector Machines
    • Kernel Methods
    • k-Means Clustering and Mixture Models for Unsupervised Learning
    • Neural Networks
    • Deep Learning
  6. Use of gradient descent, and extensions for improved scalability (stochastic gradient descent etc)
  7. Probabilistic interpretations of Machine Learning algorithms.  Maximum Likelihood and MAP estimators.
  8. Machine Learning for Recommender systems
Machine-Learning Module Homepage (TCD, Dublin, Ireland)

Machine-Learning Module Homepage

Joeran Beel

Please visit for more details about me.


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