Dynamic Systems and Models, mathematical models of systems from observations of their behavior: time series. statespace' and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence., consistency, and asymptotic distribution: relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination: order estimation; parametric identification by linear regression (least squares method, instrumental variables method, recursive algorithms); Subspace identification methods; Prediction' error methods (ARX. ARMAX, OE and BJ structures). Practical aspects of identification (input design, order estimation, model validation): Identification of Linear-Parameter Varying (LPV) models, Nonlinear system- identification, control, Adaptation and learning; Adaptive control: certainty Equivalence, Stability Issues in Time-varying Systems, stability of Adaptive Systems; Learning and Adaptation Based Control; Direct and indirect adaptive control, model reference control (MRC), model reference adaptive control (MRAC), pole placement control (PPC) and adaptive pole placement control (APPC).