Class Machine Learning Fundamentals

This course unit covers core principles of machine learning, emphasizing algorithms and models relevant to game AI.

Not applicable

Concept of learning: supervised, unsupervised and semi-supervised learning. Regression: simple and multiple linear regression, polynomial regression, regularization, SVR, decision trees, random forest regression Classification: Logistic regression, K-NN, SVM, kernel SVM, naive Bayes, decision trees, classification with random forests, one hot encoding, logistic regression, linear discriminant analysis. Dimensionality reduction: PCA, LDA, Kernel PCA Clustering: k-means, hierarchical clustering, other algorithms. Feature selection and extraction. Evaluation and generalization: training set, testing and validation, cross-validation, parameter adjustment, grid search, XGBoost. Artificial Neural Networks.

The course is theoretical and practical, alternating between teaching methods: Expository, in the presentation of the concepts. Demonstrative, in the demonstration of the concepts through examples. Participatory, in the resolution of problems with reference to the examples presented, and the use of exercise sheets to be solved autonomously by the students in class. By research, in the development of group projects.

Theobald, O. (2021). Machine Learning for Absolute Beginners : A Plain English Introduction (Third Edition). Independently Published. Burkov, A. (2019). The Hundred-Page Machine Learning Book. Independently Published. Fenner, M. (2019). Machine Learning with Python for Everyone. Pearson Education.