Schedule and organization
The contact sessions (Wednesdays 14:15-16:00 in course slack) are discussion sessions of lecture and homework exercises that the students have (preferably) solved beforehand. Recall that the lectures/materials must be studied before the sessions and the quizzes must be completed before them as well! The homework exercises are related to the lectures and hence it is advisable to watch the videos first and then do the exercises. There are two deadlines for returning the exercise answers: Rounds 1-4 on 31.7. Rounds 5-8 on 28.8. At least 50% (12/24) of homework exercises must be completed and completing at least 75% (18/24) of exercises gives a +1 grade increase to the exam. Finishing all quizzes gives one extra exercise point. The contact session topics are given in the following, the exercises are from the coursebook: 1.7.2020 - Self-study task: ODE Basics (ch. 2) Read Chapter 2 in the coursebook ODE Basics Quiz is mandatory, deadline 5.7.2020, no exercises this time Useful example code: https://github.com/AaltoML/SDE/blob/master/matlab/ch02_ex09_numerical_solution_of_odes.m 8.7.2020 - Lecture 1: Pragmatic Introduction to Stochastic Differential Equations (ch. 3) Lecture 1 Part 2: Stochastic processes in physics and engineering Lecture 1 Part 4: Heuristic solutions of non-linear SDEs, and Summary Exercise round 1: 3.1, 3.2, 3.3 Useful example codes: https://github.com/AaltoML/SDE/blob/master/matlab/ch03_ex07_time_varying_oscillator.m https://github.com/AaltoML/SDE/blob/master/matlab/ch03_ex10_stochastic_spring_model.m https://github.com/AaltoML/SDE/blob/master/matlab/ch03_fig02_two_views_of_brownian_motion.m https://github.com/AaltoML/SDE/blob/master/matlab/ch03_fig10_white_noise.m 15.7.2020 - Lecture 2: Itô Calculus and Stochastic Differential Equations (ch. 4) Lecture 2 Part 3: Solutions of linear and non-linear SDEs, Summary Exercise round 2: 4.1, 4.2, 4.6 Useful example codes: 22.7.2020 - Lecture 3: Probability Distributions and Statistics of SDEs (ch. 5) Exercise round 3: 5.1, 5.2, 5.4 29.7.2020 - Self-study task: Linear stochastic differential equations (ch. 6) Read Chapter 6 in the coursebook Exercise round 4: 6.1, 6.3, 6.8 5.8.2020 - Lecture 4: Numerical Solution of SDEs, Itô–Taylor Series, Gaussian Approximations (ch. 8 & 9) Lecture 4 part 1: Introduction, Gaussian approximations of non-linear SDEs Lecture 4 part 3: Itô-Taylor series based numerical methods, Summary Exercise round 5: 8.1, 8.2, 9.1 Useful example codes: 12.8.2020 - Lecture 5: Stochastic Runge–Kutta Methods (ch. 8) Lecture 5 part 1: Introduction, Runge–Kutta methods for ODEs Lecture 5 part 3: Weak stochastic Runge–Kutta methods, Summary Exercise round 6: 8.3, 8.4, 8.5 Useful example codes: 19.8.2020 - Lecture 6: Bayesian Inference in SDE Models (ch. 10) Lecture 6: Bayesian Inference in SDE Models - Problem Formulation Lecture 6: Continuous/Discrete-Time and Continuous Bayesian Filtering and Smoothing Exercise round 7: 10.2, 10.4, 10.5 Useful example codes: 26.8.2020 - Self-study task: Parameter estimation in SDE models (ch. 11) Read Chapter 11 in the coursebook Exercise round 8: 11.1, 11.4, 11.9 Useful example codes: