Deep Learning 1

Course manual 2025/2026

Objectives

• The student can explain and motivate the fundamental principles and mechanisms behind Deep Learning’s past, present and future
• The student can explain the major challenges, directions and active domains of research in the field of deep learning along with their advantages and disadvantages
• The student can design and execute deep learning models in a server environment
• The student can express research ideas described in publications in clear terms for readers unfamiliar with the details and devise promising follow-up research directions
• The student is able to debug and critically assess deep learning methods from a practical-engineering, and mathematical-theoretic point of view
• The student can independently tackle new deep learning problems with well-reasoned combinations of existing approaches

Teaching methods

• Lecture
• Laptop seminar
• Self-study
• Seminar

The main goal of the lecture is to introduce new materials and show the connection of the principles and methods with current research.

The goal of the practical sessions is to get the student working independently and become proficient in programming for deep learning.

Learning activities

Attendance

This programme does not have requirements concerning attendance (OER part B).

Additional requirements for this course:

This programme does not have requirements concerning attendance (OER part B).

Assessment

The grading of this course will consist of two compulsory parts: code assignments (30%) and a written exam (70%).

The goal of this structure is to ideally assess the learning outcomes while providing an inclusive learning experience that caters to a variety of students' individual strengths.

The code assignments: The final coding assignments grade will be calculated as the average of the three assignments. The code assignments are were you get to apply what you have learned in the lectures in a practical setting and get familiar with the essential tools like PyTorch and working on a server environment.

The written exam: This is a classical written exam that tests material of the whole course.

Students pass the course if the calculated grade is >=5.5+ , but the exam grade must be >=5.0. If it is below 5.0, the student may take the resit exam, which will replace the grade of the original exam. All deadlines are as outlined, late hand-ins are not accepted. In case of sickness for exams/assignments, please contact the student advisor.

Inspection of assessed work

Canvas' Ed will be kept open for discussions.

The ANS platform will be used for grading the students for assignments and exams.

Assignments

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Contact information

Coordinator

• dr. Pascal Mettes