This course will first cover the basics of linear state estimation starting from an introduction of deterministic and stochastic least square estimation. Then it will focus on Kalman filtering algorithm and its applications in remote state estimation. After that, it will introduce dynamic programming and optimal control (LQR and LQG). It will also present a brief introduction to Markov decision process and reinforcement learning.