Completed Conceptual Mapping for Machine Learning

• Day: 2025-06-24
• Time: 16:25 to 16:35
• Project: Teaching
• Workspace: WP 1: Strategic / Growth & Development
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Conceptual Mapping, Machine Learning, Model Evaluation, Alpaydin, Müller & Guido

Description

Session Goal

The session aimed to complete the conceptual mapping of Alpaydin’s work and initiate a new conceptual map based on a different book by Müller and Guido, focusing on machine learning frameworks and model evaluation techniques.

Key Activities

• Finalized the conceptual mapping of Alpaydin’s work, establishing a system to maintain traceability by author.
• Initiated a new conceptual map for machine learning, focusing on statistical and probabilistic approaches.
• Developed an initial structure for the new conceptual map, covering topics such as statistical inference, generative and probabilistic modeling, and various learning types.
• Processed and reflected on the first three fragments of Müller and Guido’s book, focusing on introduction to learning, probabilistic approaches, and cross-validation evaluation.
• Synthesized key concepts on model evaluation and adjustment, including validation methods like cross-validation and its application in Scikit-learn.
• Detailed thematic sections on model evaluation and improvement based on Müller and Guido’s work, organizing content on evaluation, cross-validation, and improvement processes.
• Summarized thematic insights from fragments 1 to 4 of Müller and Guido’s book, covering validation techniques, hyperparameter search, and advanced evaluation in supervised models.
• Detailed a refined thematic scheme on model evaluation, including sections on cross-validation, evaluation metrics, and hyperparameter search.

Achievements

• Successfully completed the conceptual mapping of Alpaydin’s work.
• Established a solid foundation for the new conceptual map based on Müller and Guido’s book.
• Clarified and organized key concepts and methods in machine learning model evaluation.

Pending Tasks

• Continue developing the conceptual map for the remaining chapters of Müller and Guido’s book.
• Further refine and expand on the thematic sections related to model evaluation and improvement.

Evidence

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• event_ids: []