• Relational Learning (cont.)
• Michalki's trains example running in Aleph
• Evaluation Metrics and Validation Methods (to be completed)
Papers to read:
• Apples-to-apples: the pitfalls of cross-validation
• The Dangers of cross-validation
• The relationship between Precision-Recall and ROC curves

• Relational Learning (cont.)
• Practical ILP using Aleph

• Rule-based systems (general rule associations)

• Bayesian Networks
• Complement: Bayesian Networks textbook slides (Bayes nets as classifiers)

• Reading (Papers ACM PKDD 2018)

• Presentations (I)

• Presentations (II)
• Handling missing values in Bayesian Networks (slides 17 and 18)
• Support Vector Machines (SVMs)
• More detailed SVMs (source)

• Neural Networks, by Prof. Jorge Oliveira

• Neural Networks (MLP algorithm and example from Poole and Mackworth's book)
• List of exercises

• Clustering and Cluster Analysis

• Weightless Artificial Neural Networks by Prof. Priscila Lima

• Time series and forecasting (talk by Prof. Luis Torgo at KDD16, available from here)
• Graph mining (available from here)

• SECOND TEST

Recommended Reading

• Evaluation
• Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms (by Thomas G. Dietterich, 1998)
• Apples-to-apples: the pitfalls of cross-validation
• The Dangers of cross-validation
• Books
• The Elements of Statistical Learning (by Trevor Hastie, Robert Tibshirani and Jerome Friedman)
• Data Analysis and Graphics using R (by John Maindonald and W. John Braun)
• Python for Probability, Statistics, and Machine Learning
• Introduction to Inductive Logic Programming (book)
• Others
• What statistical analysis should I use?
• The statistics blue book (in particular the 10 Worst Statistical Mistakes and Pitfalls)
• Data Quality and Integration Issues in Electronic Health Records
• Top 10 Algorithms in Data Mining

Links of interest