📅 2025-06-24 — Session: Conceptual Mapping of Machine Learning - Müller & Guido
🕒 16:25–16:35
🏷️ Labels: Machine Learning, Conceptual Mapping, Model Evaluation, Müller & Guido, Cross-Validation
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The session aimed to finalize the conceptual mapping of Alpaydin’s work and initiate a new conceptual map based on Müller & Guido’s book on Machine Learning.
Key Activities
- Concluded the conceptual mapping of Alpaydin’s work and established a system for organizing future maps by author to ensure traceability.
- Initiated a new conceptual map focused on Machine Learning, specifically based on Müller & Guido’s book.
- Developed an initial structure for the conceptual map, covering statistical inference, generative and probabilistic modeling, and types of learning (supervised, unsupervised, and reinforcement).
- Processed the first three fragments of Müller & Guido’s book, focusing on introduction to learning, probabilistic approach, and cross-validation evaluation.
- Synthesized key concepts about model evaluation and adjustment, including cross-validation methods and their application in Scikit-learn.
Achievements
- Successfully transitioned from Alpaydin’s mapping to Müller & Guido’s, establishing a clear framework for conceptual mapping in Machine Learning.
- Created a thematic summary and detailed breakdown of chapters 5 and 6 from Müller & Guido’s book, covering model evaluation techniques, hyperparameter tuning, and best practices.
Pending Tasks
- Continue developing the conceptual map with further insights from Müller & Guido’s book.
- Implement the refined model evaluation scheme as detailed in the session.