Resources

Course Resources

Capstone Presentation

The Innovation Diamond synthesizes all 14 ML topics into one comprehensive framework:

Full Presentation (51 slides) 10-Slide Summary (Technical) 10-Slide Summary (Practical) Student Worksheet (5 pages)

Download All Lectures

Get all 14 lecture slide decks in one download:

ZIP Archive (14 PDFs, 8 MB) Merged PDF (12 MB)

Lecture Materials

All lecture slides are available as PDF files in the course repository.

TopicFormatSourceVerifiedAccess
ML FoundationsPDF SlidesLaTeXY 2025-12-13Repository
Supervised LearningPDF SlidesLaTeXY 2025-12-13Repository
Unsupervised LearningPDF SlidesLaTeXY 2025-12-13Repository
ClusteringPDF SlidesLaTeXY 2025-12-13Repository
NLP & SentimentPDF SlidesLaTeXY 2025-12-13Repository
ClassificationPDF SlidesLaTeXY 2025-12-13Repository
Topic ModelingPDF SlidesLaTeXY 2025-12-13Repository
Generative AIPDF SlidesLaTeXY 2025-12-13Repository
Neural NetworksPDF SlidesLaTeXY 2025-12-13Repository
Responsible AIPDF SlidesLaTeXY 2025-12-13Repository
Structured OutputPDF SlidesLaTeXY 2025-12-13Repository
Validation & MetricsPDF SlidesLaTeXY 2025-12-13Repository
A/B TestingPDF SlidesLaTeXY 2025-12-13Repository
Finance ApplicationsPDF SlidesLaTeXY 2025-12-13Repository

Jupyter Notebooks

Interactive notebooks for hands-on learning. Click “Open in Colab” to run directly in your browser.

Machine Learning Fundamentals

NotebookDescriptionLink
Random ForestClassification with Random ForestOpen in Colab
Neural NetworkClassification with Neural NetworkOpen in Colab
Text EmbeddingsHuggingFace embeddings, PCA, similarityOpen in Colab

Clustering

NotebookDescriptionLink
K-Means ClusteringCustomer segmentation with K-MeansOpen in Colab
DBSCAN ClusteringDensity-based clustering for outlier detectionOpen in Colab

Data Analysis

NotebookDescriptionLink
Data ExplorationDescriptive analytics and visualizationOpen in Colab
Supervised LearningComplete supervised learning workflowOpen in Colab
———-————-——
Single Agent APIOne LLM agent making API callsOpen in Colab
Multi-Agent SystemWriter, Critic, Editor collaborationOpen in Colab
Agent CSV AnalysisAgent reads and analyzes CSV dataOpen in Colab

Handouts

Each topic includes skill-level targeted handouts as downloadable PDFs:

  • Basic: No math/code, checklists, plain English
  • Intermediate: Python implementation guides, case studies
  • Advanced: Mathematical proofs, production considerations
TopicBasicIntermediateAdvancedSource
ML FoundationsPDFPDFPDFLaTeX
Supervised LearningPDFPDFPDFLaTeX
Unsupervised LearningPDFPDFPDFLaTeX
ClusteringPDFPDFPDFLaTeX
NLP & SentimentPDFPDFPDFLaTeX
ClassificationPDFPDFPDFLaTeX
Topic ModelingPDFPDFPDFLaTeX
Generative AIPDFPDFPDFLaTeX
Neural NetworksPDFPDFPDFLaTeX
Responsible AIPDFPDFPDFLaTeX
Structured OutputPDFPDFPDFLaTeX
Validation & MetricsPDFPDFPDFLaTeX
A/B TestingPDFPDFPDFLaTeX
Finance ApplicationsPDFPDFPDFLaTeX

Python Dependencies

# Core ML and visualization
pip install scikit-learn numpy pandas scipy matplotlib seaborn

# NLP
pip install textblob transformers nltk wordcloud

# Topic Modeling
pip install gensim pyLDAvis

# Additional
pip install graphviz imblearn statsmodels

Books

  • “Hands-On Machine Learning with Scikit-Learn” by Aurelien Geron
  • “Pattern Recognition and Machine Learning” by Christopher Bishop
  • “The Design of Everyday Things” by Don Norman

Online Resources

Tools

  • Python: Primary programming language
  • Jupyter Notebooks: Interactive development
  • LaTeX/Beamer: Presentation slides
  • matplotlib/seaborn: Visualization

GitHub Repository

Full source code and materials: Digital-AI-Finance/ML_Design_Thinking

(c) Joerg Osterrieder 2025