Schedule

Lecture Slides

• Lecture 1 - Introduction
• Lecture 2 - Learning and Data Imbalance
• Lecture 3 - Extract, Transform and Load
• Lecture 4 - Bias and Quality Measures
• Lecture 5 - Extract, Transform and Load - 2
• Lecture 6 - Exeprimental Design
• Lecture 7 - Exeprimental Analysis
• Lecture 8 - Advanced Exeprimental Analysis
• Lecture 9 - Sampling and Power Analysis
• Lecture 10 - Big Data Introduction
• Lecture 11 - In-memory Parallel Processing in Spark
• Lecture 12 - Data Warehouse Design in Spark
• Lecture 13 - Information Visualization
• Lecture 14 - Explaining Models
• Reinforcement Learning