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8.30h - 9.30h
Registration, coffee/tea and croissants
Introduction to this course, the speaker and the participants
1. Model Development Introduction
- Current Trends in AI, Machine Learning and Predictive Analytics
- Algorithms in the News: Deep Learning
- The Modeling Software Landscape
- The Rise of R and Python: The Impact on Modeling and Deployment
- Do I Need to Know About Statistics to Build Predictive Models?
2. Strategic and Tactical Considerations in Binary Classification
- What's is an Algorithm?
- Is a "Black Box" Algorithm an Option for Me?
- Issues Unique to Classification Problems
- Why Classification Projects are So Common
- Why are there so many Algorithms?
3. The Tasks of the Model Phase
- Model Assessment
- Evaluate Model Results: Check Plausibility and Reliability
- Model Accuracy and Stability
- Why Accuracy and Stability are Not Enough
- What to Look for in Model Performance
- What are Potential Deployment Challenges for Each Candidate Model?
4. What is Unsupervised Learning?
- Why most organisations utilize unsupervised methods poorly:
- Case Study 1: Finding a new opportunity
- Case Studies 2, 3, and 4: How do supervised and unsupervised work together
- Data Preparation for Unsupervised
- The importance of standardization
- Running an analysis directly on transactional data
- Unsupervised Algorithms:
- Hierarchical Clustering
- Self-Organizing Maps
- K Nearest Neighbors
5. Wrap-up and Next Steps
- Supplementary Materials and Resources
- Conferences and Communities
- Get Started on a Project!
This course is a pre-conference workshop of the BI & Data Analytics Summit, a large conference in Belgium with 2 keynote speakers, 12 case-based presentations, and over 100 participants.
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