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teaching:ws2024:dlpractice [2024/06/28 14:14] (current) Dr. Vladimir Golkov created |
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| + | ~~NOTOC~~ | ||
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| + | ===== Practical Course: Expert-Level Deep Learning (10 ECTS) ===== | ||
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| + | ** <fc # | ||
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| + | **Please send applications (including learning goals, programming skills description, | ||
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| + | **Organizers**: | ||
| + | [[members: | ||
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| + | Preliminary meeting (attendance **not** obligatory): | ||
| + | Slides from an earlier semester' | ||
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| + | < | ||
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| + | Details about the matching system can be found [[http:// | ||
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| + | < | ||
| + | If you ask for a spot after the matching phase, but do not hear from us soon, it means that we cannot offer you a spot. | ||
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| + | == Content == | ||
| + | In this course, we will develop deep learning algorithms for concrete applications in the field of computer vision, biomedicine, | ||
| + | The topics will include: | ||
| + | * Machine learning, deep learning | ||
| + | * Standard and advanced neural network architectures | ||
| + | * Tasks beyond supervised learning | ||
| + | * Design of architectures, | ||
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| + | The projects will be geared towards developing novel solutions for <fc # | ||
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| + | **If you want to propose an own project instead of choosing from the projects that we will offer, please discuss with us before 16 July. Use the email subject " | ||
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| + | == Prerequisites == | ||
| + | Good programming skills. Eagerness to acquire and deepen knowledge about how to solve complex problems with machine learning. Passion for mathematics. The course will be focused on practical projects, thus previous knowledge of Python and array programming in NumPy (or in Matlab or similar) is desired. Having also good soft skills (or the willingness to acquire them quickly) and using them is a prerequisite. | ||
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| + | Knowledge of deep learning is recommended/ | ||
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| + | Important soft skills include communication skills, the ability to identify what is unclear, to figure out what questions need to be asked to clarify it, to formulate the questions clearly, and to ask the tutor without hesitation. The ability to communicate strategically is an important prerequisite of the practical course. | ||
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| + | == Course Structure == | ||
| + | The students will work individually and in groups on practical deep learning projects. At the end of the project, each student or group will present their project with a following Q&A session. There will be no additional written or oral exam. Both the theoretical and practical part of the project will be considered in the final grading. | ||
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| + | //Note: ECTS credits are the measure of workload. So-called semester weekly hours (Semesterwochenstunden, | ||
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| + | == Introductory Lectures == | ||
| + | The students will receive recordings of introductory lectures as early as they want.\\ | ||
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| + | Lecture 1: Machine Learning; Artificial Neural Networks; Convolutional Neural Networks; Q&A about Deep Learning\\ | ||
| + | Lecture 2: Recap; Network Architecture Design; Q&A about Deep Learning\\ | ||
| + | Lecture 3: Recap; Network Training; Understanding and Visualizing; | ||
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| + | == Literature == | ||
| + | * [[https:// | ||
| + | * [[http:// | ||
| + | * [[https:// | ||
| + | * [[http:// | ||
| + | * Good current papers | ||
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