Lecture time  Wednesday 1012  

Exercise time  Friday 1012  
Lecture rooms  Wednesdays: E1.4 Room 024; Fridays: E1.4 Room 023 

Lecturer  Timo Kötzing  
Content  3,5,7,11,13,... what's next? What general rule (apparently) produces this sequence? Maybe the sequence lists all the odd primes, but what if the next datum is 15? Maybe all odd numbers that are not squares? In this course we will study learning (identification) of infinite objects (such as infinite sequences) from finite data (such as initial pieces of the sequence), also known as Inductive Inference. What (collections of) sequences can be learned? What does learning, or identification, actually mean? We will discuss and compare several notions of "identification." The main focus lies on exploring the limits of what can be learned algorithmically. 

Prerequisites  If you can write proofs and at least 2 of the following statements are true for you, then you fulfill the requirements of the course. Statements: I know the Turingmachine model (or some other abstract computation model). I know what "computably enumerable" means. I know what "halting problem" refers to.  
Credits  The grade will be determined based on the performance in the exercises and a (short) final exam. Successful participation will earn you 6 credits.  
Exercises  There will be exercises every week. These exercises will be discussed in the exercise session of the following week. It is permissable to work on the exercises in groups of two people.  
Lecture Notes  I provide my lecture notes for your convenience. I believe that all assigments are solvable with the lecture notes and not other material.  
Final Exam  The final exam will be a short oral exam; I compiled a list of areas for your convenience.  
Lesson Plan 
