slds-lmu/lecture_sl
Mar 2025 – Jul 2025
5 Months active
Languages Used
LaTeXRMarkdown
Technical Skills
Data VisualizationMachine LearningR ProgrammingTechnical WritingMachine Learning VisualizationMathematical Notation
Ziyu-Mu
PROFILE
Ziyu-mu
Ziyu Mu enhanced the slds-lmu/lecture_sl repository by developing and refining educational materials for machine learning lectures, focusing on loss function visualizations, bias-variance analysis, and nonlinear model interpretation. Using R, LaTeX, and data visualization techniques, Ziyu introduced new plots, standardized mathematical notation, and automated asset generation to improve clarity and maintainability. The work included adaptive axis scaling for regularized models, restoration and synchronization of slide assets, and code reorganization to streamline updates. These contributions addressed both feature development and bug fixes, resulting in more reliable, consistent, and accessible teaching resources for students and instructors across multiple release cycles.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
9Total
Bugs
2
Commits
9
Features
4
Lines of code
5,762
Activity Months5
Your Network
9 people
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for slds-lmu/lecture_sl: Delivered feature-focused improvements to nonlinear model visualization and slide presentation polish. Implemented adaptive axis scaling for weight plots and histograms depending on regularization, and refined LaTeX slide generation with updated frame titles and image inclusion methods. No separate bugs fixed this period; focus was on enhancements that increase clarity, reliability, and presentation quality. The work strengthens data storytelling for stakeholders and enables faster, more confident decisions.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for slds-lmu/lecture_sl: Delivered feature-focused improvements to nonlinear model visualization and slide presentation polish. Implemented adaptive axis scaling for weight plots and histograms depending on regularization, and refined LaTeX slide generation with updated frame titles and image inclusion methods. No separate bugs fixed this period; focus was on enhancements that increase clarity, reliability, and presentation quality. The work strengthens data storytelling for stakeholders and enables faster, more confident decisions.
July 2025
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025: Focused on elevating the quality and maintainability of lecture slides in slds-lmu/lecture_sl. Delivered quality improvements by standardizing citation links and reorganizing assets in the advriskmin directory, supported by new generation scripts and outputs to streamline asset production and presentation clarity. Changes were delivered through commits 637a872105a549ede5a5204ded41b18aa968279d (citelink_update) and d111b2eb73010749df4c5cf47cd9388106f98b83 ('adv_fig_code_clean'). This work reduces manual update effort, improves consistency for learners, and strengthens reproducibility for future revisions.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025: Focused on elevating the quality and maintainability of lecture slides in slds-lmu/lecture_sl. Delivered quality improvements by standardizing citation links and reorganizing assets in the advriskmin directory, supported by new generation scripts and outputs to streamline asset production and presentation clarity. Changes were delivered through commits 637a872105a549ede5a5204ded41b18aa968279d (citelink_update) and d111b2eb73010749df4c5cf47cd9388106f98b83 ('adv_fig_code_clean'). This work reduces manual update effort, improves consistency for learners, and strengthens reproducibility for future revisions.
May 2025
2 Commits
May 1, 2025Month: May 2025 — Repository: slds-lmu/lecture_sl. Focused on improving content integrity and ensuring up-to-date assets for teaching slides. Key features delivered: fixes to LaTeX formatting in chapter 11 and restoration of slide PDFs to upstream main version to ensure correct rendering and current assets. These changes reduce rendering issues and asset drift, improving reliability for students and instructors. Major bugs fixed: LaTeX formatting issues in chapter 11 and slide PDF drift; assets now aligned with upstream. Overall impact: improved content integrity, consistent slide rendering across environments, and reduced QA/support overhead; aligns local materials with upstream for long-term maintainability. Technologies/skills demonstrated: LaTeX; PDF rendering; asset/version synchronization; Git commit hygiene and targeted bug fixes in educational content pipelines.
2 Commits
May 1, 2025Month: May 2025 — Repository: slds-lmu/lecture_sl. Focused on improving content integrity and ensuring up-to-date assets for teaching slides. Key features delivered: fixes to LaTeX formatting in chapter 11 and restoration of slide PDFs to upstream main version to ensure correct rendering and current assets. These changes reduce rendering issues and asset drift, improving reliability for students and instructors. Major bugs fixed: LaTeX formatting issues in chapter 11 and slide PDF drift; assets now aligned with upstream. Overall impact: improved content integrity, consistent slide rendering across environments, and reduced QA/support overhead; aligns local materials with upstream for long-term maintainability. Technologies/skills demonstrated: LaTeX; PDF rendering; asset/version synchronization; Git commit hygiene and targeted bug fixes in educational content pipelines.
May 2025
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025: Delivered targeted visual analytics improvements in the slds-lmu/lecture_sl project, focusing on bias-variance interpretation and consistent plotting. Implemented new Mean Squared Error (MSE) plots and refined visuals for clearer model performance insights, and standardized Bernoulli loss notation ('log' instead of 'ln') across R scripts and figures. These updates enhance decision-support for model evaluation and align with the project’s visualization standards.
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025: Delivered targeted visual analytics improvements in the slds-lmu/lecture_sl project, focusing on bias-variance interpretation and consistent plotting. Implemented new Mean Squared Error (MSE) plots and refined visuals for clearer model performance insights, and standardized Bernoulli loss notation ('log' instead of 'ln') across R scripts and figures. These updates enhance decision-support for model evaluation and align with the project’s visualization standards.
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary focused on advancing loss-function education materials in the slds-lmu/lecture_sl repo. Delivered a feature enhancement: visualizations and R script updates for loss functions used in classification and regression. The updates introduce new plots to illustrate Bernoulli loss, Brier score, L1 loss, hinge loss, and log-cosh loss, improving educational clarity and accuracy.
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary focused on advancing loss-function education materials in the slds-lmu/lecture_sl repo. Delivered a feature enhancement: visualizations and R script updates for loss functions used in classification and regression. The updates introduce new plots to illustrate Bernoulli loss, Brier score, L1 loss, hinge loss, and log-cosh loss, improving educational clarity and accuracy.
March 2025
Activity
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Quality Metrics
Correctness97.8%
Maintainability97.8%
Architecture95.6%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
LaTeXMarkdownR
Technical Skills
Code OrganizationData VisualizationDocumentationFile ManagementLaTeXMachine LearningMachine Learning VisualizationMathematical NotationPresentation GraphicsRR ProgrammingRegularizationTechnical Writing
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