Lecturer: Lucian Busoniu, TA: Zoltan Nagy
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The course introduces nonparametric methods for system identification, as well as parametric methods including prediction error and instrumental variables techniques. Input signals, online recursive methods, and model validation are also discussed. The material is described at an appropriate BSc level, and builds in a self-contained manner the required mathematical background. The course is based on the book System Identification by Soderstrom and Stoica.
This course is part of the Bachelor program of the Automation Department, UTCluj (3rd year 1st semester). Prerequisites: linear dynamical systems and linear algebra. The lecturer is Lucian Busoniu, who also teaches the project classes, while Zoltan Nagy teaches the lab classes for the English line.
The lab solutions, the two lab tests, and a project solution are all required before being admitted to the exam. Note that while the lab solutions are not graded, they are still required; they are validated by the TA during the lab. It is therefore not enough to be present at the lab in order to validate it; a complete, working, and original solution must be developed during the lab. At most two labs can be recovered at the end of the semester; hence, accumulating three or more missing labs means you can no longer become eligibile for the exam during the current year.
You can check your current status in the Google sheet table here, updated in near-real-time.
Dates and locations:
Due to interdependencies between lectures, labs, and project classes, as well as other reasons, the actual schedule is slightly different from the official one. For instance, during the test weeks, we will need to use project slots for the test as well, so that everyone has time to take the test. Please look at the schedule carefully to determine exactly when you should be in class.
Everything takes place in the room indicated in the official schedule. For the labs and lectures, the official timeslot is always kept. For the project, sometimes we need to rearrange odd/even weeks (but the hour never changes). Specifically, the labels "odd" and "even" in the table refer to the official table, so "odd" includes 1.2 (group 1 halfgroup 2), 2.1, and 3.1, while "even" includes 1.1, 2.2, and 3.2. You should come to the project class when you see your label in the table; for example, even though week 7 (11-15 November) is odd, since the table says "3 even", all the even halfgroups (1.1, 2.2, and 3.2) should come to the project class during that week and work on project session 3. All this should be feasible given your current schedule; if incompatible changes occur, we will revisit the planning.
5 Dec 2019: a small change, we are skipping the lecture in week 12 (on Dec 18th). This means lectures 12-13 move to week 13-14; this most likely creates no additional workload for the students, as lecture 13 is just a backup and usually the material is finished in 12 lectures.
The lecture slides are mandatory reading; they will be written down in detail to give a self-contained, complete picture of the topics. They are made available here in time for each lecture.
At the end of each lecture except the first, a 5-minute quiz with three questions is given from that lecture. So, you should pay attention during the lectures. If you answer all the questions correctly, you get 1 point in your grade; lower scores scale linearly to between 0 and 1 points in the grade.
In addition to the slides, followers may optionally consult the following books:
Each lab except the first starts with a 3-minute quiz containing 2 short questions, which tests you on the material relevant to that particular lab. If you answer everything correctly for all the labs, you get 1 point in your grade; lower scores scale linearly, as for the lecture quizzes. So, you should arrive prepared.
Each student is assigned a randomly chosen technique studied in the labs, and a randomly chosen dataset; the student must then apply the technique to the dataset. The solution consists of Matlab code, which must be submitted at the end of the test, and will be run and verified by the lecturer. The test is 1 hour long, and the solution must be developed in the first 50 minutes; the last 10 minutes are reserved for discussing the solution with the lecturer. To ensure the test is solvable in the time slot allowed, some techniques can be applied in a simplified manner. These simplifications will be explicitly indicated in the test material.
You are free to use the course material, including lecture slides, lab descriptions and data. All this will be available offline on the computer on which you will take the test. The complete Matlab documentation is of course also available. However, the internet connection is disabled on the computer and you are not allowed to use internet-connected devices, or to reuse the solutions you developed for the labs.
For the first test, the slots are assigned as in the column "Test 1 slot" in the online table, where the format is: day in the week of Nov 18-22 / hour. The test is scheduled during your normal lab slot, as far as possible -- some exceptions may be made due to computer availability. Please be on time; the one-hour interval cannot be exceeded since your colleagues are starting the test immediately after that. If for well-founded reasons you cannot make it on the assigned slot, please contact the lab assistant as soon as possible with a request to change the slot. Reassignments cannot be made if they result in more than 8 students taking the test in the same time slot.
See the project description (PDF) for the topic, rules, and deadlines. Please read it carefully. A detailed description of the linear regression method needed in the first part of the project can be found in last year's lecture; this year we talk about it in lecture 4.
The schedule for part 2 presentations has been published.
Comments, suggestions, questions etc. related to this course or website are welcome; please contact either the lecturer or the TA at the addresses below (given as images for spambot protection).