WEEK 14: WRAP-UP AND PROJECT PRESENTATIONS

An Application of the tools studied so far: Deep Uncertainty Quantification for Integrated Assessment Models, Wrap-up of the lecture, Voluntary project presentations, 2h Consulting for the capstone project

December 15, 2025

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Weekly Materials

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Lecture slides, week 14

Professor's lecture slides (PDF)
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Wrap-up of the lecture

Professor's lecture slides (PDF)
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TA Practice Slides

Hands-on tutorials and practice exercises

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Capstone Project

Final capstone project submission

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Code Examples

Finger exercises (at the end of the lecture; self-study) Multiple Demo Files
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References & Resources

Capstone Project Guidelines Every student has to provide a capstone project that illustrates what was learned

Additional Notes

Week 14: Wrap-up

Overview

The final week of the Advanced Data Analytics course brings together all the concepts learned throughout the semester. This session includes advanced applications, course wrap-up, voluntary project presentations, and capstone project consultation.

Learning Objectives

  • Understand advanced applications of machine learning techniques
  • Apply deep uncertainty quantification to complex real-world problems
  • Synthesize knowledge from all previous weeks
  • Present and discuss capstone projects
  • Plan for continued learning and professional development

Topics Covered

  • Deep Uncertainty Quantification: Advanced application for Integrated Assessment Models
  • Course Wrap-up: Review of key concepts and methodologies
  • Voluntary Project Presentations: Student showcase of capstone projects
  • Capstone Project Consulting: Individual guidance and feedback (2h session)

Schedule

  • Lecture: Monday, December 15, 2025 (10:15 - 12:00)
  • Project Presentations: Monday, December 15, 2025 (14:00 - 16:00)
  • Capstone Consulting: Monday, December 15, 2025 (16:30 - 18:30)

Advanced Application Focus

  • Integrated Assessment Models: Climate and economic modeling applications
  • Uncertainty Quantification: Bayesian approaches to model uncertainty
  • Deep Learning for Scientific Computing: Neural networks in complex simulations
  • Real-world Impact: How ML techniques address global challenges

Course Synthesis

  • Journey Review: From basic ML to advanced applications
  • Methodology Integration: How different techniques complement each other
  • Problem-Solving Framework: Systematic approach to ML projects
  • Best Practices: Lessons learned and professional guidelines

Capstone Project Components

Every student must provide a comprehensive capstone project demonstrating course learning:

  • Technical Implementation: Applied ML solution to a real problem
  • Code Repository: Well-documented GitHub repository
  • Project Report: Detailed technical report
  • Presentation: Clear communication of methods and results

Voluntary Presentations

Students may present their capstone projects covering:

  • Problem definition and motivation
  • Technical approach and methodology
  • Implementation details and challenges
  • Results and impact assessment
  • Future work and extensions

Consulting Session

Individual 2-hour consulting sessions provide:

  • Personalized feedback on capstone projects
  • Technical guidance and troubleshooting
  • Career advice and next steps
  • Research opportunities and directions

Course Completion

Final requirements for course completion:

  • All exercises submitted and evaluated
  • Capstone project completed and submitted
  • Active participation in course activities
  • Demonstration of learning objectives achievement

Looking Forward

  • Advanced machine learning courses and specializations
  • Research opportunities in ML and data science
  • Industry applications and career paths
  • Continuous learning resources and communities