Hauptinhalt
Topinformationen
Mitarbeiterverzeichnis
Foundations of Machine Learning: Theory and Practice
Prof. Dr. Elia Bruni
Veranstaltungstyp: Seminar
TeilnehmerInnen:
Beschreibung:
This seminar provides an introduction to the foundations of machine learning, covering essential concepts, algorithms, and techniques in traditional machine learning. If there will be time left, we wll quickly look at basic Neural Networks, but that is not guaranteed.
Participants will gain a deep understanding of supervised, unsupervised, and semi-supervised learning, as well as insights into statistical machine learning and key optimization methods. The course emphasizes both theory and practical implementation, preparing students for advanced machine learning applications and research.
Machine Learning Algorithms' Categories:
Supervised, unsupervised, and semi-supervised learning, classification, regression, clustering, parametric vs. non-parametric, and linear vs. nonlinear approaches.
Supervised Learning:
Linear regression, logistic regression, support vector machines, linear discriminant analysis, decision trees, ensemble methods (random forest, boosting, XGBoost).
Optimization and Loss Functions:
Gradient descent, variations (SGD, Momentum, RMSprop, ADAM), logistic loss, cross-entropy, hinge loss.
Model Evaluation and Selection:
Metrics (accuracy, precision, recall, F-score, ROC curve, AUC), confusion matrix, handling imbalanced datasets, cross-validation.
Unsupervised Learning:
Clustering (k-means, DBSCAN, hierarchical clustering), Gaussian Mixture Models, dimension reduction techniques (PCA, ICA, t-SNE).
Bayesian algorithms:
Naive Bayes, Maximum a posteriori (MAP) estimation, Maximum Likelihood (ML) estimation
Statistical significance:
R-squared, P-values
Data Handling:
Missing data, imbalanced data, data distribution shifts
Similarity/dissimilarity metrics:
Euclidean, Manhattan, Cosine, Mahalanobis (advanced)
Erstes Treffen:
Dienstag, 29.04.2025 14:00 - 17:00, Ort: 35/E01
Ort: 35/E01: Di. 14:00 - 17:00 (10x), 32/102: Di. 14:00 - 17:00 (2x)
Semester: SoSe 2025
Zeiten:Di. 14:00 - 17:00 (wöchentlich), Ort: 35/E01, 32/102
Leistungsnachweis:
- Coding assignments - Final exam
Veranstaltungsnummer:
8.30807
ECTS-Kreditpunkte:
4
Bereichseinordnung:
Veranstaltungen > Cognitive Science > Bachelor-Programm Veranstaltungen > Cognitive Science > Master-Programm Courses Open to Exchange Students > Human Sciences (e.g. Cognitive Science, Psychology)
Prof. Dr. Elia Bruni
Veranstaltungstyp: Seminar
TeilnehmerInnen:
Beschreibung:
This seminar provides an introduction to the foundations of machine learning, covering essential concepts, algorithms, and techniques in traditional machine learning. If there will be time left, we wll quickly look at basic Neural Networks, but that is not guaranteed.
Participants will gain a deep understanding of supervised, unsupervised, and semi-supervised learning, as well as insights into statistical machine learning and key optimization methods. The course emphasizes both theory and practical implementation, preparing students for advanced machine learning applications and research.
Machine Learning Algorithms' Categories:
Supervised, unsupervised, and semi-supervised learning, classification, regression, clustering, parametric vs. non-parametric, and linear vs. nonlinear approaches.
Supervised Learning:
Linear regression, logistic regression, support vector machines, linear discriminant analysis, decision trees, ensemble methods (random forest, boosting, XGBoost).
Optimization and Loss Functions:
Gradient descent, variations (SGD, Momentum, RMSprop, ADAM), logistic loss, cross-entropy, hinge loss.
Model Evaluation and Selection:
Metrics (accuracy, precision, recall, F-score, ROC curve, AUC), confusion matrix, handling imbalanced datasets, cross-validation.
Unsupervised Learning:
Clustering (k-means, DBSCAN, hierarchical clustering), Gaussian Mixture Models, dimension reduction techniques (PCA, ICA, t-SNE).
Bayesian algorithms:
Naive Bayes, Maximum a posteriori (MAP) estimation, Maximum Likelihood (ML) estimation
Statistical significance:
R-squared, P-values
Data Handling:
Missing data, imbalanced data, data distribution shifts
Similarity/dissimilarity metrics:
Euclidean, Manhattan, Cosine, Mahalanobis (advanced)
Erstes Treffen:
Dienstag, 29.04.2025 14:00 - 17:00, Ort: 35/E01
Ort: 35/E01: Di. 14:00 - 17:00 (10x), 32/102: Di. 14:00 - 17:00 (2x)
Semester: SoSe 2025
Zeiten:Di. 14:00 - 17:00 (wöchentlich), Ort: 35/E01, 32/102
Leistungsnachweis:
- Coding assignments - Final exam
Veranstaltungsnummer:
8.30807
ECTS-Kreditpunkte:
4
Bereichseinordnung:
Veranstaltungen > Cognitive Science > Bachelor-Programm Veranstaltungen > Cognitive Science > Master-Programm Courses Open to Exchange Students > Human Sciences (e.g. Cognitive Science, Psychology)
