Data-driven model predictive control using DMD (DS4DS 7.07)

Differential predictive control: introduction (DS4DS 7.08)

Optimal control in discrete time (DS4DS 7.02)

Linear model predictive control (DS4DS 7.06)

Optimal control of linear systems 1 - Dynamics (DS4DS 7.03)

Optimal control - Introduction (DS4DS 7.01)

Optimal control of linear systems 2 - Objective function (DS4DS 7.04)

Optimal control of linear systems 3 - Example (DS4DS 7.05)

Differential predictive control: global vs. local optima (DS4DS 7.09)

Overfitting and the bias-variance tradeoff (DS4DS 6.01)

Cross validation (DS4DS 6.02)

Sparsity and the L1 norm (DS4DS 6.03)

Subdifferentials of non-differentiable functions (DS4DS 6.04)

The LASSO algorithm for model selection (DS4DS 6.05)

Sparse identification of nonlinear dynamics (SINDy) (DS4DS 6.06)

Feature Engineering - Introduction (DS4DS 5.01)

Extracting linear features from data (DS4DS 5.03)

Manual feature selection using dictionaries (DS4DS 5.02)

The role of features in deep learning (DS4DS 5.05)

Feature engineering using autoencoders (DS4DS 5.04)

Hammerstein-Wiener models in NODEs (DS4DS 4.09)

Discrete-time nonlinear system identification (DS4DS 4.11)

Neural ordinary differential equations - NODEs (DS4DS 4.07)

Sampled input data for NODE modeling (DS4DS 4.08)

Extrapolation of data-driven dynamical models (DS4DS 4.10)

Utilizing constrained optimization for incorporating a priori knowledge (DS4DS 4.06)

Identification of an unknown initial state (DS4DS 4.05)

Global vs. local optima in nonlinear data-driven modeling (DS4DS 4.04)

Impact of the prediction horizon in data-driven modeling (DS4DS 4.02)

Nonlinear prediction error method identification (DS4DS 4.03)