This short course provides the participants with the basic concepts of model-based signal processing using an applied approach. The course is designed to take the participant from basic probability and random processes to stochastic model development through the heart of physics-based stochastic modeling---the Gauss-Markov state-space model. Estimation basics will be discussed including maximum likelihood and maximum a-posteriori estimators. The state-space model-based processor (MBP) or equivalently Kalman filter will be investigated theoretically in order to develop an intuition for constructing successful MBP designs using the "minimum error variance approach". Practical aspects of the MBP will be developed to provide a reasonable approach for design and analysis. Overall MBP Design Methodology will be discussed. Extensions of the MBP follow for a variety of cases included prediction, colored noise, identification, linearized and nonlinear filtering using the extended Kalman filter. Applications and case studies will be discussed throughout the lectures including the tracking problem along with an application suite MBP problems. Practical aspects of MBP design using SSPACK_PC, a third party toolbox in MATLAB, will be discussed for "tuning" and processing along with some actual data. In summary, this course not only provides the participants with the essential theory underlying model-based signal processing techniques, but applied design and analysis.