Dr. Pawel Herman

Associate Professor

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Biography

Pawel Andrzej Herman is an Associate Professor at KTH Royal Institute of Technology, specializing in the field of Machine Learning and its applications in Information Technology. With a solid educational foundation in Computer Science Engineering, he teaches several courses including Artificial Neural Networks and Deep Architectures, and is responsible for various teaching assignments. His research interests include the exploration of Machine Learning techniques, particularly in Probabilistic Theory and Statistical Applications. He is actively involved in educating future engineers and providing insights into the dynamics of Human Perception through his coursework. His scholarly contributions can be tracked via his ORCID profile, showcasing his commitment to advancing knowledge in his field.

Research Interests

Machine Learning Artificial Neural Networks Deep Architectures Human Perception Probability Theory Statistical Applications

Courses

Artificial Neural Networks and Deep Architectures (DD2437) Artificial Neural Networks and deep Architectures (FDD3437) Degree Project Computer Science Engineering, Cycle (DA150X) Human Perception Information Technology (DM2350) Probability Theory Statistics Application Machine Learning (SF1935) Program Integrating Course in Machine Learning (DD2301)

Requirements for KTH Royal Institute of Technology

Master Program
Requirements
GPA Requirement
Required:3
IELTS
Listening
Required:5.5
Reading
Required:5.5
Writing
Required:5.5
Speaking
Required:5.5
Overall
Required:6.5
TOEFL
Listening
Required:0
Reading
Required:0
Writing
Required:20
Speaking
Required:0
Total
Required:90
PTE
Listening
Required:0
Reading
Required:0
Writing
Required:62
Speaking
Required:0
Overall
Required:62
Prerequisites
Bachelor's degree (180 ECTS) in Science, Engineering, or a related field. Specific course requirements vary by department (e.g., Mathematics, Programming, or Subject-specific credits).
Application Checklist
  • Certificates and diplomas of completed degree
  • Transcript of records
  • Proof of English proficiency
  • Copy of passport or ID
  • Curriculum Vitae (CV)
  • Letter of Motivation
  • Letters of Recommendation (if required by program)
  • Summary of Project (if required)
Specialization Notes

Master's programs are organized under Schools; departments listed are units within these schools (e.g., EECS, ABE, CBH, ITM, SCI).