During the this semester I am teaching the course “Markov decision theory” at the Department of Mathematics. The course presents the algorithmic aspects of Markov decision theory and illustrates the wide applicability of this theory to a variety of realistic problems. First, the course consider the Poisson process followed by Renewal/Reward Processes. Next we study the theory of Markov chains. Then follows a thorough presentation of the theory of Markov Decision Processes, including some applications. Finally, further applications are discussed based on reports made by the students. A course plan is given here.
One of my PhD students, for who I am a co-supervisor, has just finished the defense of her thesis titled “Methods for Sensor Based Farrowing Prediction and Floor-heat Regulation: The Intelligent Farrowing Pen“. I would like to congratulate Aparna with her PhD title and for a good thesis and presentation at the defense. I have included the thesis summary below.