Admission requirements
Description
Many concepts in physics, specifically the statistical mechanics underpinning of thermodynamics, have proven to have a wider applicability in the science of information. This course explores this interplay between these two fields in the area of machine learning. We discuss machine learning as a generalization of the thermodynamics of spin systems and optimization as the search for a physical ground state. The interplay between physics and machine learning becomes clear in analyzing optimal Deep Neural Networks as those that sit on an edge of chaos phase transition; how the optimal state in Deep Neural Networks saturates the maximal possible information bottleneck bound, and how formally Deep Neural Networks are equivalent to disordered quantum field theories. Alternatively, using machine learning insights for physics, Restricted Boltzmann Machines are shown to be highly efficient variational wavefunctions for densely entangled quantum groundstates. The recent Large Language Models are shown to be equivalent to self-re-inforcing spin systems.
Course Objectives
The course will consist of two parts:
The first part of the course will consist of a set of conventional lectures and (graded) assignments;
The second part of the course will consist of a set of indepedent research projects with a written report and presentations by the students;
Timetable
For detailed information go to Brightspace
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
See Brightspace
Assessment method
The final course grade will be determined from
Graded assignments (50%)
Project Presentation (20%)
Project report (30%)
Reading list
All materials (this syllabus, problem assignments, background material) can be found on the Leiden University Brightspace site: Brightspace
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Lecturers: Prof.dr. K.E. Schalm Dr. D.F.E. Samtleben Dr. M. Schaller
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.