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teaching:ws2019:dlpractice_ws2019 [2019/07/13 12:51] Dr. Vladimir Golkov |
teaching:ws2019:dlpractice_ws2019 [2019/11/05 10:58] Dr. Vladimir Golkov |
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** Winter Semester 2019/2020, TU München ** | ** Winter Semester 2019/2020, TU München ** | ||
+ | |||
+ | **Please send applications (including learning goals, programming skills description, | ||
**Organizers**: | **Organizers**: | ||
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The preliminary meeting (**not** obligatory) takes place on **Monday, 8 July 2019 at 4pm** in [[https:// | The preliminary meeting (**not** obligatory) takes place on **Monday, 8 July 2019 at 4pm** in [[https:// | ||
- | Slides from the preliminary meeting can be downloaded {{: | + | Slides from the preliminary meeting |
+ | |||
+ | |||
+ | < | ||
+ | </ | ||
+ | < | ||
- | < | ||
== Course Description == | == Course Description == | ||
- | This course is targeted at excellent students who are on their path to being experts and making key contributions to the progress of science and technology. | + | In this course, we will develop deep learning |
+ | The topics will include: | ||
+ | * Machine learning, neural networks, deep learning | ||
+ | * Standard | ||
+ | * Tasks beyond supervised learning | ||
+ | * Design of architectures, choice of loss functions, tuning of hyperparameters. | ||
- | The projects will be geared towards developing novel solutions for < | + | The projects will be geared towards developing novel solutions for < |
**If you want to propose an own project rather than choosing from the projects that we will offer, please discuss with us before 20 July 2019.** | **If you want to propose an own project rather than choosing from the projects that we will offer, please discuss with us before 20 July 2019.** | ||
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== Prerequisites == | == Prerequisites == | ||
- | * Excellent | + | 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 |
- | * Eagerness to acquire and deepen knowledge about how to solve complex problems with machine learning | + | |
- | * Solid skills in mathematics | + | |
- | * Good literature-reading/ | + | |
- | * Solid knowledge of deep learning theory | + | |
- | * Python and array programming in NumPy (or Matlab or similar) | + | |
- | * PyTorch (or TensorFlow or similar) | + | |
- | * Small gaps in the skills above are OK, because having all other skills means that gaps can be filled quickly | + | |
- | * Good soft skills (or the willingness to acquire them quickly) | + | |
- | Choice among many different projects will be offered. Some projects will be about biomedicine. Prior knowledge | + | Knowledge of deep learning and computer vision is recommended/ |
- | 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 supervisor | + | 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. Communicating well and strategically is an important rule of the practical course. Almost all difficulties experienced by students are due to not following these rules. |
- | == Application | + | == Course Structure |
+ | In the first three weeks, there will be lectures every week, focusing on theoretical and practical concepts related to deep learning. During the semester, the students will work in groups on practical deep learning projects. Each group will consist of about 1-2 students, and will be supervised by one of the tutors. At the end of the semester, each 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. The course schedule is detailed below. | ||
- | Step 1: Send an email to dlpractice[at]vision.in.tum.de < | ||
- | * Your interests, learning goals | ||
- | * Description of your skills (see prerequisites) | ||
- | * Some code you wrote in any context | ||
- | * All grade transcripts | ||
- | * Ongoing courses | ||
- | |||
- | < | ||
- | </ | ||
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- | Step 2: Apply through the [[https:// | ||
- | |||
- | Students who did not register or did not get matched can contact us at dlpractice[at]vision.in.tum.de | ||
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- | == Course Structure == | ||
- | In the first three weeks, there will be lectures every week, focusing on theoretical and practical concepts related to deep learning. During the semester, the students will work in groups and individually on practical deep learning projects. At the end of the semester, each 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. | ||
== Course Schedule == | == Course Schedule == | ||
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22 October: Recap; Network Architecture Design; Understanding and Visualizing; | 22 October: Recap; Network Architecture Design; Understanding and Visualizing; | ||
29 October: Recap; Programming; | 29 October: Recap; Programming; | ||
- | 05 November: Q&A about Deep Learning\\ | ||
//Note: ECTS credits are the measure of workload. So-called semester weekly hours (Semesterwochenstunden, | //Note: ECTS credits are the measure of workload. So-called semester weekly hours (Semesterwochenstunden, |