Name Dept. Course # Section Term Instructors Notes
Statistics for Life Scientists Grad School 16/2
Cremer, Sylvia
Molecules, Cells, and Models Grad School 17/1
Jonas, Peter
Applications of stochastic processes Grad School 17/3
Barton, Nicholas
IST core course Grad School 17/7
Barton, Nicholas
Novarino, Gaia
Hausel, Tamas
Deep learning with tensorflow Grad School 17/8
Lampert, Christoph
Maths For Quantitative LS: Introduction To Differential Equations Grad School 18/1
Merrin, Jack
Solid State Physics Grad School 18/2
Serbyn, Maksym
Protein Physics Grad School 18/3
Ivankov, Dmitrii
Graduate Student's Books Grad School 18/4
The Stability of Matter in Quantum Mechanics Grad School 18/5
Applications of Stochastic Processes Grad School 19/1
Barton, Nicholas
Population genetics - the basics Grad School 19/2
Barton, Nicholas
Algebraic Methods in Combinatorics Grad School 19/3
Wagner, Uli
Introduction to Algebraic Geometry Grad School 19/4
Hausel, Tamas
Graph Theory Grad School 19/5
Arroyo Guevara, Alan
Statistical Machine Learning Grad School 20/1
Lampert, Christoph

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