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)