Artificial Intelligence for Managers

The course is intended for all people who want to understand the principles of machine learning and artificial intelligence without diving into technical details.

None.

• What is machine learning and artificial intelligence
  • Difference between artificial intelligence and machine learning
  • High-level basics of machine learning
  • Most important machine learning tasks
  • What cannot be solved by machine learning
• Data-driven company
  • Importance of data
  • Intuition vs. data
  • Exponential advantage
  • Bridge between managers and developers
• Machine learning metrics and interpretability
  • Training vs. test data sets
  • Accuracy, precision, recall, RMSE, MAE, R-Square
  • Data outliers
  • Data imbalance
  • Confidence of ML models
  • Explainability of machine learning decisions
• A/B testing and understanding its results
  • Traditional A/B testing
  • Multi-armed bandits for optimization
  • Simultaneous A/B testing of multiple features
  • Confidence intervals
• Ethics, safety and security
• Machine learning practical use cases
  • Text (classification, sentiment analysis, summarization, reasoning, chat bots)
  • Images and video (classification, segmentation, superresolution, denoising)
  • Recommendation systems and sorting
  • Time series predictions (machine trading, e-commerce)
  • Anomaly detection