Topic outline

  • General

    Exams

    There will be two exam dates from which you may pick only one. So, it's either or.

    The first option: Tuesday, November 8, 5 pm - 8 pm, lecture hall C in the main building. (There are always these optional codes for lecture halls. In this case it is Y205. Anyway, you should find the lecture hall by just the "name" C.)

    In this first exam you should not be needing a calculator. You may taka a basic function calculator with you, though, if you like.

    The second option: Monday, December 12, 9 am - 12 pm, lecture hall T1 in the computer science building.

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    Please note: In order to have your registration for this or any other course accepted in Sisu, the course must be included in your study plan. Check that this is the case and also check that you have the latest version of your study plan in place.

    Please also note: Professors or lecturers don't have permission to enrol students in Sisu. So, if you need to register after the deadline for the course in Sisu, please send a message to grades-sci@aalto.fi. You can cc me, Riku Linna (riku.linna@aalto.fi), just in case if the people in learnng services need a confirmation from me. If registering in Sisu cannot be done right away, send an e-mail to me, and I will add you to MyCourses (provided you have an aalto e-mail).

    Lecturer: Riku Linna (riku.linna@aalto.fi)

    Teaching Assistants:  

    • Danh Phan (danh.phan@aalto.fi) 
    • Nicola Dainese (nicola.dainese@aalto.fi)
    • Thao Phung Duc (thao.phungduc@aalto.fi)


    Lectures, Tuesdays 12:15

    Lecture hall D/ Y122, "Kandidaattikeskus" (Main Building). This is what it says in Sisu, to my surprise, maybe they thought the classroom in the CS building is too small. If the location changes, I will send an announcement.

    Exercise sessions, Thursdays 10:15

    Join Zoom Meeting
    https://aalto.zoom.us/j/9439582763

    Meeting ID: 943 958 2763

    While you are welcome to join, attendance is not required. The course is taken by returning weekly assignments and grading two or three assignment submissions weekly, using Peergrade and taking the exempt the end. Instructions for joining the course Peergrade can be found under Materials. Deadlines for submission of solutions, peer grading, and feedback can be found under Assignments. 20 % of the points come from peer grading, that is, how our grading is evaluated, and the rest from the graded solutions.

    Please note that since the assignments are peer-graded, unfortunately, you cannot come back next year and return possibly similar/same problems for grading.

    Grading The grade is determined by the exercises (appr. 70 %) and the exam (appr. 30 %).  Peer grading will constitute 20 % of the exercise points.

    Please subscribe to the General discussion forum - under 'Forums' top right corner, for important announcements. Otherwise, Slack workspace, https://aalto-cms-2022.slack.com, will be mainly used for communication. To join Slack, please use this invitation. There you can ask questions, help others, etc. Please contact T.A via email if you face any problems in joining Slack. You are allowed and, in fact, encouraged to solve problems together. Just make sure that your solutions are not complete copy-pastes of someone else's.

    The purpose of this course is to provide an understanding of fundamental concepts and computational methods of stochastic simulations and models. After completing the assignments the student will have a library of (skeleton) algorithms used in stochastic simulation and an understanding of how they work.


    Topics include:

    1. Simulating standard probability distributions. 

    2. Methods of simulating 'non-standard' distributions. Logarithmic binning.

    3. Markov processes and stochastic models.

    4. Monte Carlo (MC) method and Metropolis sampling.

    5. Markov Chain Monte Carlo (MCMC) method; Gibbs and Metropolis-Hastings sampling.

    6. Hamiltonian/Hybrid Monte Carlo (HMC) method.

    Literature: Parts of the books Taylor, Karlin (newer edition Pinsky, Karlin): An Introduction to Stochastic Modeling (Academic Press), and Wilkinson: Stochastic Modelling for Systems Biology (CRC Press). Lecture notes and other distributed material.

    The book Hossein Pishro-Nik, Introduction to Probability and Random Processes, freely available online will be used in parts of the course: https://www.probabilitycourse.com 


    Prerequisites: 

    Basic programming skills. The programming language is Python. Jupyter notebook will be used. 

    For some practicalities etc., see Preliminaries in Materials.