Introduction
Evaluating AI models correctly is critical to ensuring they perform reliably and safely. This course covers evaluation metrics, benchmarking practices, and model comparison techniques for a wide range of AI applications. Participants will learn how to design meaningful tests and interpret results properly. Real-world case studies highlight challenges such as dataset drift and bias. By the end, learners will be prepared to assess AI systems with confidence.
Course Objectives
- Understand evaluation metrics across tasks
- Learn proper benchmarking methodology
- Compare model performance accurately
- Identify common evaluation pitfalls
- Perform end-to-end benchmarking exercises
Target Audience
- ML engineers
- Data scientists
- AI researchers
- QA/testing engineers
- Students learning model evaluation
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
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- Day 1: Evaluation Foundations• Why evaluation matters
• Types of metrics
• Performance vs. robustness
• Dataset splits
• Hands-on: Basic evaluation demo0 - Day 2: Classification & Regression Metrics• Accuracy, precision, recall
• ROC curves
• RMSE, MAE
• Confusion matrix interpretation
• Hands-on: Evaluate classification models0 - Day 3: NLP & Vision Metrics• BLEU, ROUGE, perplexity
• IoU, FID, PSNR
• Human evaluations
• Multi-task evaluation
• Hands-on: Evaluate NLP/CV models0 - Day 4: Benchmarking Practices• Standard datasets
• Ablation studies
• Baseline comparisons
• Distribution shift testing
• Hands-on: Run a benchmark suite0 - Day 5: Advanced Evaluation Topics• Fairness and bias assessment
• Robustness and adversarial testing
• Monitoring in production
• Limitations of benchmarks
• Capstone evaluation project0
Instructor
Lessons:
