Course content
- Historical development and evolution of AI/ML
- Key terminology
- Linear algebra and probability review
- Theoretical underpinnings of learning from data
- How energy functions and loss functions guide model training and evaluation
- Various loss functions
- First-order optimization: gradient descent (GD) and stochastic gradient descent (SGD)
- Basics of constrained optimization and its relevance in training
- Hyperparameter tuning strategies
- Validation techniques to assess model generalization
- Evaluation metrics to measure and compare models
- Bayesian inference in machine learning