Lecture and tutorial classes
Machine learning in quantum physics
(Winter term 2018/2019)
Quantum machine learning is an emerging research field.
This first course focuses on the subfield where classical machine learning is applied in quantum physics.
The goal of this course is to provide the students with the necessary skills to understand the main ideas and some details of the ongoing research.
Content
Introduction to artificial neural networks and deep learning
Applications in quantum physics
Exercises and other files will be uploaded here.
Formal things
Lecture and tutorial class (Übung) have been moved to
Tuesday 8:30am, room 25.33.00.61 (lecture)
Friday 8:30am, room 25.33.00.61 (tutorial class)
Assignments (click here) will be uploaded every two weeks.
The solutions to the assignment sheets need to be handed in.
As a prerequisite for the exam, at least 70% of the assignment sheets need to be finished.
There will be no corrections but the solutions will be discussed in the tutorial classes.
Note:
Due to an unexpected high number of course participants student presentations turned out not to be feasible.
Preliminary schedule: 30 lectures (VL) and problem classes (Ü)
October (8)
09  VL (Motivation, course outline, formalities), exercise 1 handed out
12  Ü (Introduction, student questions & discussion)
16  VL (General ML & statistics)
19  Ü due date: discussion of exercise 1, exercise 2 handed out
23  VL (General ML & statistics)
26  Ü, (Student questions & discussion)
30  VL (General ML & statistics); due date: exercise 2, exercise 3 handed out
November (8)
02  Ü (discussion of exercise 2)
06  VL (DL)
09  Ü (Student questions & discussion)
13  VL (DL); due date: exercise 3, exercise 4 handed out
16  Ü (discussion of exercise 3)
20  VL (DL)
23  Ü (Student questions & discussion)
27  VL (RBMs); due date: exercise 4, exercise 5 handed out
30  Ü (discussion of exercise 4)
References
