These lectures convey concepts of machine learning, especially about time series applications. This is a specialization course, and the successful completion of "IntroPR," "Pattern Recognition," and/or "Pattern Analysis" is recommended.
The following topics are covered in the lesson:
- Time series fundamentals and definitions (2 lectures)
- Bayesian Inference (1 lecture)
- Gaussian processes (2 lectures)
- State-space models (2 lectures)
- Autoregressive models (1 lecture)
- Data mining on time series (1 lecture)
- Deep learning on time series (4 lectures)
- Domain adaptation (1 lecture)
