We offer multiple courses at the University of Freiburg which are listed below. Besides the University, we offer a free massive open online course (MOOC) on AutoML. For more information please visit the course website.
Course type: B.Sc./M.Sc. projects and theses Web page: See Student page for details on open projects and theses
Course type Oberseminar: Reading Group (no ECTS) -- Automated Machine Learning Time: Wednesdays, 13:00 Web page: Reading Group
Course type: Oberseminar: Reading Group (no ECTS) -- Deep Learning and Hyperparameter Optimization Time: Wednesdays, 16:00 Location: online Contact: Samuel Müller ,
Course type: Flipped-classroom Lecture + Exercise Time: Tuesday 10:00 - 12:00 Location: HS 00 036 SCHICK - SAAL (G.-Köhler-Allee 101) Organizers: Joschka Bödecker , Daniel Stefan Matthias Büsher , Tim Welschehold , Steven Adriaensen Web page: TBA
This course will introduce basic concepts and techniques used within the field of Artificial Intelligence. Among other topics, we will discuss:
- Introduction to and history of Artificial Intelligence
- Agents
- Problem solving and search
- Logic and knowledge representation
- Planning
- Representation of and reasoning with uncertainty
- Machine learning
Lectures will roughly follow the book: Artificial Intelligence a Modern Approach
Course type: Flipped-classroom Lecture + Exercise Time: Monday 14:00 - 16:00 Location: SR 01-009/13 (G.-Köhler-Allee 101) Organizers: Frank Hutter , André Biedenkapp , Noor Awad , Heri Rakotoarison ,Neeratyoy Mallik , Eddie Bergman Web page: HISinOne
Background
Applying machine learning (ML) and in particular deep learning (DL) in practice is a challenging task and requires a lot of expertise. Among other things, the success of ML/DL applications depends on many design decisions, including an appropriate preprocessing of the data, choosing a well-performing machine learning algorithm and tuning its hyperparameters, giving rise to a complex pipeline. Unfortunately, even experts need days, weeks or even months to find well-performing pipelines and can still make mistakes when optimizing their pipelines.The course will discuss meta-algorithmic approaches to automatically search for, and obtain well-performing machine learning systems by means of automated machine learning (AutoML). Such AutoML systems allow for faster development of new ML/DL applications, require far less expert knowledge than doing everything from scratch and often even outperform human developers. In this lecture, you will learn how to use such AutoML systems, to develop your own systems and to understand ideas behind state-of-the-art AutoML approaches.
Requirements
We strongly recommend that you know the foundations of
- machine learning (ML)
- and deep learning (DL)
We further recommend that you have hands-on experience with:
- Python (3.8+)
- machine learning
- deep learning
The participants should have attended at least one other course for ML and DL in the past.
Topics
The lectures are partitioned in several parts, including:
- Hyperparameter Optimization
- Bayesian Optimization for Hyperparameter Optimization
- Neural Architecture Search
- Dynamic Configuration
- Analysis and Interpretability of AutoML
- Algorithm Selection/Meta-Learning
Organization
The course will be taught in a flipped-classroom style. We will meet weekly once for a combined Q/A session and an exercise. Roughly every week, there will be a new exercise sheet. Most exercises will be practical, involve programming in python and teamwork (teams of up to 3 students!) so that you learn how to apply AutoML in practice.
Lecture/Exercise: Monday 14 - 16
The material is publicly available via the AI-Campus platform . Please register on AI-Campus to access the materials. Grading of the exercises will be done via GitHub classroom. The links to access the GitHub classroom repositories will be made available at the beginning of the semester. (The password to that page will be announced in the first session). The course will be taught in English.
Course type: Lecture + Exercise Time: Monday, 14:15 - 15:45, first meeting: Oct. 17 Location: The course will be Hybrid:
- Weekly flipped classroom sessions will be held on Monday at HS 00 026 µ - SAAL (G.-Köhler-Allee 101) and via Zoom. See ILIAS for Zoom link.
- Optional exercise sessions will take place on Friday 10:15-11:45 at HS 00 006 (G.-Köhler-Allee 082) Organizers: Frank Hutter , Abhinav Valada , Steven Adriaensen , Mahmoud Safari Web page: ILIAS (please make sure to also register for all elements of this course module in HISinOne)
Foundations of Deep Learning
Deep learning is one of the fastest growing and most exciting fields. This course will provide you with a clear understanding of the fundamentals of deep learning including the foundations to neural network architectures and learning techniques, and everything in between.
Course Overview
The course will be taught in English and will follow a flipped classroom approach.
Every week there will be:
- a video lecture
- an exercise sheet
- a flipped classroom session (hybrid, Mondays 14:15 - 15:45)
- an attendance optional exercise session (in-class/offline, Fridays 10:15 - 11:45)
At the end, there will be a written exam (likely an ILIAS test).
Exercises must be completed in groups and must be submitted a week (+ 1 day) after their release.
Your submissions will be graded and you will receive weekly feedback.
Your final grade will be solely based on a written examination, however, a passing grade for the exercises is a prerequisite for passing the course.
Hybrid course: All material will be made available online and course participation will not require in-person presence. That being said, we offer ample opportunity for direct interaction with the professors during live Q & A sessions (HS 00 026 µ - SAAL, G.-Köhler-Allee 101) and with our tutors during weekly attendance optional in-class exercise sessions (HS 00-006, G.-Köhler-Allee 082).
Exam: The exam will likely be a test you complete on ILIAS. In-person presence may be required (TBA).
Course Schedule
The following are the dates for the flipped classroom sessions:
17.10.22 - Kickoff: Info Course Organisation / Team Formation
24.10.22 - Week 1: Intro to Deep Learning
31.10.22 - Week 2: From Logistic Regression to MLPs
07.11.22 - Week 3: Backpropagation
14.11.22 - Week 4: Optimization
21.11.22 - Week 5: Regularization
28.11.22 - Week 6: Convolutional Neural Networks (CNNs)
05.12.22 - Week 7: Recurrent Neural Networks (RNNs)
12.12.22 - Week 8: Attention & Transformers
19.12.22 - Week 9: Practical Methodology
09.01.23 - Week 10: Hyperparameter Optimization
16.01.23 - Week 11: Neural Architecture Search
23.01.23 - Week 12: Auto-Encoders, Variational Auto-Encoders, GANs
30.01.23 - Week 13: Uncertainty in Deep Learning
06.02.23 - Round-up / Exam Q & A
The course material (lecture video, slides, exercise sheet) for "Week N" will be made available a week before the flipped classroom session for "Week N". For example, the material for Week 1 will be available on 17.10.22 and solutions to the exercises must be submitted latest 25.10.22 at 23:59. Virtual participation in flipped classroom sessions will be enabled using Zoom and the meeting link can be found on ILIAS in the "Flipped Classroom" folder.
In the first session (on 17.10.22) you will get additional information about the course and get the opportunity to ask general questions (and form groups!) While there is no need to prepare for this first session, we encourage you to already think about forming teams.
The last flipped classroom session will be held on 06.02.23.
Questions?
If you have a question, please post it in the ILIAS forum (so everyone can benefit from the answer).
Alternatively, you can also email dl-orga-ws22@cs.uni-freiburg.de
Course Type: Lab Course Time & Location: 10.10.22 - 13.10.22 participation in the AutoML Fall School
19.10.22; 14:00-16:00; 082 HS 00 006 Introduction to Projects
During the semester : Implement your own AutoML system
TBA : Poster presentation Organizers: André Biedenkapp , Rhea Sukthanker , Frank Hutter Web Page: HISinOne , Local Page
Note: You do not need to register for the fall school if you have registered for this course. Your participation in the fall school will be free of charge. You can register for the course via HISinOne .
Background
Applying machine learning (ML) and in particular deep learning (DL) in practice is a challenging task and requires a lot of expertise. Among other things, the success of ML/DL applications depends on many design decisions, including an appropriate preprocessing of the data, choosing a well-performing machine learning algorithm and tuning its hyperparameters, giving rise to a complex pipeline. Unfortunately, even experts need days, weeks or even months to find well-performing pipelines and can still make mistakes when optimizing their pipelines. The lab course will start with a fall school featuring lectures on hot topics in AutoML such as "automating data science", "automated reinforcement learning" or "neural architecture search", as well as present tutorials on various topics in AutoML. After this fall school, students are tasked with implementing their own AutoML system for a particular task or problem domain.
Requirements
We require that you have heard a lecture on
- Machine Learning, and/or
- Deep Learning
We strongly recommend that you have taken the AutoML lecture.
Organization
This lab course officially starts one week before the start of the lecture period. During that week, students take part in the AutoML fall school , to be held in Freiburg, to hear from world leading AutoML experts about current hot topics in the field. After this week, we will meet to present potential projects from which the students are free to select which one they want to tackle. We expect that students work in groups of up to three to implement their AutoML system. During the semester the students will meet with a supervisor to discuss potential issues they are facing when implementing their system. At the end of the semester all groups will present their work during a poster presentation.
Grading
The grades are determined based on the quality of the project part.
Important Dates
- 10.10.22 - 13.10.22: AutoML Fall School
- 19.10.22 14:00 - 16:00: Introduction to the topics In: HS 00 006 (G.-Köhler-Allee 082)
- TBD: Poster Session
Kickoff Slides
Course type: Seminar Time: Tuesdays 14:00 - 16:00 Location: G.-Köhler-Allee 051, R 03 026 Organizers: André Biedenkapp , Noor Awad , Frank Hutter Web page: HISinOne , Local Page
You can register for the seminar via HISinOne .
Background
Hyperparameter optimization is a powerful approach to achieve the best performance on many different problems. However, automated approaches to solve this problem tend to ignore the iterative nature of many algorithms. With the dynamic algorithm configuration (DAC) framework we can generalize over prior optimization approaches, as well as handle optimization of hyperparameters that need to be adjusted over multiple time-steps. In this seminar, we will discuss applications (such as temporally extended epsilon greedy exploration in RL ) and domains (e.g., reinforcement learning , evolutionary algorithms or deep learning ) that can benefit from dynamic configuration methods. A large portion of the seminar will be dedicated to discussing papers that describe DAC methods that employ reinforcement learning to learn hyperparameter optimization policies for various domains.
Requirements
We require that you have taken lectures on
- Machine Learning, and/or
- Deep Learning
We strongly recommend that you have heard lectures on
- Automated Machine Learning
- Reinforcement Learning
Organization
Every week all students read the relevant literature. Two students will prepare presentations for the topics of the week and present it in the session. After each presentation, we will have time for a question & discussion round and all participants are expected to take part in these. Each student has to write a short paper about their assigned topic which is to be handed in one week after their presentation.
Grading
- Presentation: 40% (20min + 20min Q&A)
- Paper: 40% (4 pages in AutoML Conf format , due one week after your presentation)
- Participation in Discussions: 20%
Schedule
Fair and Interpretable Machine Learning
| Course type: | Seminar |
| Time: | Wednesday 16:00 - 18:00 |
| Location: | G.-Köhler-Allee 051, SR 00 034 |
| Organizers: | Janek Thomas , Noor Awad |
| Web page: | HisInOne |
Seminar on Fair and Interpretable Machine Learning
The seminar language will be English (even if everyone is German-spoken, to practice presentation skills in English).
*First meeting:
- 27th April, 16:00-18:00, SR 00 034 (G.-Köhler-Allee 051).
*Regular meetings:
- Every Wednesday, 16:00-18:00, SR 00 034 (G.-Köhler-Allee 051).
Background
The seminar focuses on interpretable machine learning in the first half of the semester and fair machine learning in the second half.
We will discuss model-agnostic tools for fairness and interpretability as well as specific algorithms. Further topics include measuring fairness, a causal perspective on fairness and how to consider fairness in AutoML.
Organization
Each week: All Students read relevant literature. Three students prepare the topic with slides and applications. Three other students are assigned as discussants. Discussants have to meet with the group presenting prior to the session, give feedback and prepare critical discussion points and open questions.
End of the semester: Each student has to write a short paper (10 pages) about their topic.
Requirements
We strongly recommend that you know the foundations of
- Machine Learning
- For some topics: Deep Learning
Main Literature
[1] Molnar, Christoph. Interpretable machine learning. Lulu. com, 2020. - https://christophm.github.io/interpretable-ml-book/
[2] Mehrabi, Ninareh, et al. "A survey on bias and fairness in machine learning." ACM Computing Surveys (CSUR) 54.6 (2021): 1-35.
[3] Barocas, Solon, Moritz Hardt, and Arvind Narayanan. "Fairness in machine learning." Nips tutorial 1 (2017): 2.
Schedule
| Date | Topic | Main Ref. | Further Refs. |
| 27.04.2022 | Introduction, groups and topic assignments | - | |
| 04.05.2022 | - | ||
| 11.05.2022 | - | [1] Chapter 1-3 | |
| 18.05.2022 | Interpretable Machine Learning Methods | [1] Chapter 5 | Friedman, Jerome H., and Bogdan E. Popescu. "Predictive learning via rule ensembles." The annals of applied statistics 2.3 (2008): 916-954. |
| Lou, Yin, et al. "Accurate intelligible models with pairwise interactions." Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 2013. | |||
| Hofner, Benjamin, et al. "A framework for unbiased model selection based on boosting." Journal of Computational and Graphical Statistics 20.4 (2011): 956-971. | |||
| 25.05.2022 | Global Model-Agnostic Interpretability Methods | [1] Chapter 8 | Apley, Daniel W., and Jingyu Zhu. “Visualizing the effects of predictor variables in black box supervised learning models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82.4 (2020): 1059-1086. |
| Wei, Pengfei, Zhenzhou Lu, and Jingwen Song. "Variable importance analysis: a comprehensive review." Reliability Engineering & System Safety 142 (2015): 399-432. | |||
| Kim, Been, Rajiv Khanna, and Oluwasanmi O. Koyejo. "Examples are not enough, learn to criticize! criticism for interpretability." Advances in neural information processing systems 29 (2016). | |||
| 01.06.2022 | Local Model-Agnostic Interpretability Methods | [1] Chapter 9 | Goldstein, Alex, et al. "Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation." journal of Computational and Graphical Statistics 24.1 (2015): 44-65. |
| Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "" Why should I trust you?" Explaining the predictions of any classifier." Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016. | |||
| Karimi, Amir-Hossein, et al. "Model-agnostic counterfactual explanations for consequential decisions." International Conference on Artificial Intelligence and Statistics. PMLR, 2020. | |||
| 08.06.2022 | - | [3] Chapter 1 | |
| 15.06.2022 | Interpretability Methods for Neural Networks | [1] Chapter 10 | Zeiler, Matthew D., and Rob Fergus. "Visualizing and understanding convolutional networks." European conference on computer vision. Springer, Cham, 2014. |
| Kim, Been, et al. "Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)." International conference on machine learning. PMLR, 2018. | |||
| Jain, Sarthak, and Byron C. Wallace. "Attention is not explanation." arXiv preprint arXiv:1902.10186 (2019). | |||
| 22.06.2022 | Multi-Objective and constrained Optimization and Model Selection | TBD | Deb, Kalyanmoy, et al. "A fast and elitist multiobjective genetic algorithm: NSGA-II." IEEE transactions on evolutionary computation 6.2 (2002): 182-197. |
| Knowles, Joshua. "ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems." IEEE Transactions on Evolutionary Computation 10.1 (2006): 50-66. | |||
| Gardner, Jacob R., et al. "Bayesian Optimization with Inequality Constraints." ICML. Vol. 2014. 2014. | |||
| 29.06.2022 | Measures for Fairness | [2] Section 4.1 | Hardt, Moritz, Eric Price, and Nati Srebro. "Equality of opportunity in supervised learning." Advances in neural information processing systems 29 (2016). |
| Kearns, Michael, et al. "An empirical study of rich subgroup fairness for machine learning." Proceedings of the conference on fairness, accountability, and transparency. 2019. | |||
| Chouldechova, Alexandra. "Fair prediction with disparate impact: A study of bias in recidivism prediction instruments." Big data 5.2 (2017): 153-163. | |||
| 06.07.2022 | Debiasing Methods | [2] Section 5.1 | Mehrabi, Ninareh, et al. "Debiasing community detection: The importance of lowly connected nodes." 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2019. |
| Calmon, Flavio, et al. "Optimized pre-processing for discrimination prevention." Advances in neural information processing systems 30 (2017). | |||
| Kamiran, Faisal, and Toon Calders. "Data preprocessing techniques for classification without discrimination." Knowle... |
Course type: Flipped-classroom Lecture + Exercise Time: Wednesday 12:00 - 14:00 Location: HS 00 036 SCHICK - SAAL (G.-Köhler-Allee 101) Organizer: Janek Thomas , André Biedenkapp , Edward Bergman , Neeratyoy Mallik , Frank Hutter Web page: Link to Lecture Page
Background
Applying machine learning (ML) and in particular deep learning (DL) in practice is a challenging task and requires a lot of expertise. Among other things, the success of ML/DL applications depends on many design decisions, including an appropriate preprocessing of the data, choosing a well-performing machine learning algorithm and tuning its hyperparameters, giving rise to a complex pipeline. Unfortunately, even experts need days, weeks or even months to find well-performing pipelines and can still make mistakes when optimizing their pipelines.The course will discuss meta-algorithmic approaches to automatically search for, and obtain well-performing machine learning systems by means of automated machine learning (AutoML). Such AutoML systems allow for faster development of new ML/DL applications, require far less expert knowledge than doing everything from scratch and often even outperform human developers. In this lecture, you will learn how to use such AutoML systems, to develop your own systems and to understand ideas behind state-of-the-art AutoML approaches.
Requirements
We strongly recommend that you know the foundations of
- machine learning (ML)
- and deep learning (DL)
We further recommend that you have hands-on experience with:
- Python (3.6+)
- machine learning
- deep learning
The participants should have attended at least one other course for ML and DL in the past.
Topics
The lectures are partitioned in several parts, including:
- Hyperparameter Optimization
- Bayesian Optimization for Hyperparameter Optimization
- Neural Architecture Search
- Dynamic Configuration
- Analysis and Interpretability of AutoML
- Algorithm Selection/Meta-Learning
Organization
The course will be taught in a flipped-classroom style. We will meet weekly once for a combined Q/A session and an exercise. Roughly every week, there will be a new exercise sheet. Most exercises will be practical, involve programming in python and teamwork (teams of up to 3 students!) so that you learn how to apply AutoML in practice.
Lecture/Exercise: Wednesday 12:00 - 14:00
The material is publicly available via the AI-Campus platform . Please register on AI-Campus to access the materials. Grading of the exercises will be done via GitHub classroom. The links to access the GitHub classroom repositories are available here . (The password to that page will be announced in the first session). The course will be taught in English.
Exam
Student conference:
“Students groups of three write a short report on one of three potential topics, peer-review the reports and present their work in a virtual poster session.”
- Report: 4 pages + #students pages appendix
- Contributions of each student needs to be described for the report
- Paper template will be provided
- Will be organized on openreview.net
- Peer review:
- Each student has to write a review for one other paper
- We will provide some reviewing guidelines
- Poster Conference:
- Jointly with Hannover
- Online in gather.town
- Oral presentation of your poster during a predefined time-slot
- 20.07.2022: Project handout
- 31.08.2022: Report Deadline
- 09.09.2022: Review Deadline
- 13.09.2022 (16:00 CEST): Poster Submission Deadline (a template is available here )
- 15 - 16.09.2022: Poster Session
- 23.09.2022: Final Deadline (incorporate feedback from the poster session)
Conference Schedule
| Day | Time Slot | Poster Room | Paper IDs |
| Thursday 15.09.22 | 09:00 - 10:45 | Room 1 | 9, 16, 20, 24, 25 |
| Thursday 15.09.22 | 11:00 - 12:30 | Room 2 | 6, 7, 11, 12, 27 |
| Thursday 15.09.22 | 13:30 - 15:30 | Room 3 | 2, 13, 14, 15, 28, 30 |
| Friday 16.09.22 | 09:00 - 11:30 | Room 1 | 1, 3, 4, 5, 17, 18, 19 |
Please make sure to be at your poster during the time-slot above. While it is not your turn to present a poster, you can visit the other posters and discuss the approaches with the presenters. You are expected to take part in the whole conference. If you're unsure of your paper ID, you can check on openreview.
| Course type | Oberseminar: Reading Group (no ECTS) -- Automated Machine Learning |
| Time: | Wednesdays, 13:00 |
| Web page: |
Reading Group |
| Course type: | Oberseminar: Reading Group (no ECTS) -- Deep Learning and Hyperparameter Optimization |
| Time: | Wednesdays, 16:00 |
| Location: | online |
| Contact: | Samuel Müller , |
| Course type: | B.Sc./M.Sc. projects |
| Kickoff meeting: | TBA |
| Location: | online only |
| Web page: |
Open Projects |
Update Oct. 14: We added details about the seminar mode.
Update Oct. 8: Title and time of the seminar changed & added the link to HISinOne.
| Course type: | Seminar |
| Time: | Tuesday, 10:00 - 11:30 am, first meeting: Oct. 19 |
| Location: | The course will be fully virtual/online. See ILIAS for Zoom link. |
| Topic & Mode: | In this year's seminar, we will discuss papers about Self-Supervised Learning with strong focus on computer vision. The details about the seminar mode will be discussed in the kick-off meeting on Oct 19th. We will begin our seminar with a background video that we discuss together on Oct 2nd. For the following sessions students are asked to: 1) present a paper, 2) lead the moderation on the paper, 3) actively participate in the discussion of other paper presentations. We will try to assign the papers based on your preference but there's no guarantee you will receive the paper you requested. |
| Organizer: | Frank Hutter , Fabio Ferreira , Samuel Müller , Robin Schirrmeister |
| Web page: | ILIAS , HISInOne |
| Course type: | Lecture + Exercise |
| Time: | Wednesday, 12:15 - 13:45, first meeting: Oct. 20 |
| Location: |
The course will be fully virtual/online. Weekly flipped classroom sessions will be held on Zoom. See ILIAS for Zoom link. |
| Organizers: | Frank Hutter , Abhinav Valada , Steven Adriaensen , Samuel Müller , Yash Mehta , Niclas Vödisch |
| Web page: | ILIAS (please also register for all elements of this course module in HISinOne ) |
Foundations of Deep Learning
Deep learning is one of the fastest growing and most exciting fields. This course will provide you with a clear understanding of the fundamentals of deep learning including the foundations to neural network architectures and learning techniques, and everything in between.Course Overview
The course will be taught in English and will follow a flipped classroom approach. Every week there will be:- a video lecture
- an exercise sheet
- a flipped classroom session (virtual/online, Wednesdays 12:15 - 13:45)
- an attendance optional exercise session (in-class/offline, Thursdays 10:15 - 11:45)
At the end, there will be a written exam (likely ILIAS test).
Exercises must be completed in groups and must be submitted a week (+ 1 day) after their release. Your submissions will be graded and you will receive weekly feedback. Your final grade will be solely based on a written examination, however a passing grade for the exercises is a prerequisite for passing the course.
Online course: All material will be made available online and course participation will not require in-person presence. That being said, we offer the opportunity for direct interaction with our tutors during weekly attendance optional in-class exercise sessions (building 82, HS 00-006). In addition, it is possible to attend the digital flipped classroom sessions on campus using your own laptop + headphones (Building 101 - HS 00 036).
Exam: The exam will likely be a test you complete on ILIAS. In person presence may be required (tba).
Course Schedule
The following are the dates for the flipped classroom sessions:20.10.21 - Kickoff: Info Course Organisation / Team Formation
27.10.21 - Week 1: Overview of Deep Learning
03.11.21 - Week 2: From Logistic Regression to MLPs
10.11.21 - Week 3: Backpropagation
17.11.21 - Week 4: Optimization
24.11.21 - Week 5: Regularization
01.12.21 - Week 6: Convolutional Neural Networks (CNNs)
08.12.21 - Week 7: Recurrent Neural Networks (RNNs)
15.12.21 - Week 8: Practical Methodology & Architectures
22.12.21 - Week 9: Hyperparameter Optimization
12.01.22 - Week 10: Neural Architecture Search
19.01.22 - Week 11: Attention & Transformers
26.01.22 - Week 12: Auto-Encoders, Variational Auto-Encoders, GANs
02.02.22 - Week 13: Uncertainty in Deep Learning
09.02.22 - Round-up / Exam Q & A
The course material (lecture video, slides, exercise sheet) for "Week N" will be made available a week before the flipped classroom session for "Week N". For example, the material for Week 1 will be available on 20.10.21 and solutions to the exercises must be submitted latest 28.10.21 at 23:59. We will be using Zoom and the meeting link can be found on ILIAS in the "Flipped Classroom" folder.
In the first session (on 20.10.21) you will get additional information about the course and get the opportunity to ask general questions (and form groups!) While there is no need to prepare for this first session, we encourage you to already think about forming teams. The last flipped classroom session is held on 09.02.22.
Competition Results
This semester, we organised an optional student competition. In this challenge, students were to train a model to perform class prediction on a flower dataset (more info here ).There were two tracks:
- Fast-track (models with less than 100k parameters)
- Large-track (models with less than 25M parameters)
The winners per track were determined based on the accuracy of the submitted models on a hidden test set.
The Fast-track podium:
- 1st place: Nisarga Nilavadi Chandregowda, Pablo Marhoff, Tidiane Ndir (accuracy: 90.49%)
- 2nd place: Bijay Gurung, Caoting Li, Kartik Yadav (accuracy: 82.80%)
- 3rd place: Uygar Akkoc, Aron Bahram, Samir Garibov (accuracy: 73.12%)
The Large-track podium:
- 1st place: Paweł Bugyi, Abhijeet Nayak, Preethi Sivasankaran (accuracy: 95.15%)
- 2nd place: Akshay Chandra Lagandula, Sai Prasanna Raman, John Robertson (accuracy: 92.49%)
- 3rd place: Bijay Gurung, Caoting Li, Kartik Yadav (accuracy: 91.28%)
Congratulations!
Questions?
If you have a question, please post it in the ILIAS forum (so everyone can benefit from the answer).Alternatively, you can also email dl-orga-ws21@cs.uni-freiburg.de
| Course type: | B.Sc./M.Sc. projects |
| Kickoff meeting: | TBA |
| Location: | online only |
| Web page: |
Open Projects |
| Course type: | Lab Course |
| Time: | Tuesday, 14:00-16:00 (Beginning Apr 20, 2021) |
| Location: | online only |
| Organizer: | Frank Hutter , Fabio Ferreira , Jörg Franke , Arbër Zela , Samuel Müller , Yash Mehta |
| Web page: | Website , HISinOne |
| Course type: | Flipped-classroom Lecture + Exercise |
| Time: | Wednesday 14:00 - 16:00 |
| Location: | online only |
| Organizer: | Frank Hutter André Biedenkapp , Gresa Shala |
| Web page: |
AutoML |
| Course type: | Oberseminar: Reading Group (no ECTS) -- Deep Learning and Hyperparameter Optimization |
| Time: | Wednesdays, 16:00 |
| Location: | online |
| Contact: | Samuel Müller , |
| Course type | Oberseminar: Reading Group (no ECTS) -- Automated Machine Learning |
| Time: | Wednesdays, 13:00 |
| Web page: |
Reading Group |
| Course type: | B.Sc./M.Sc. projects |
| Location: | Kitchen, Building 074 |
| Web page: | Open Projects |
| Course type: | Seminar |
| Time: | Wednesday, 12:30 - 2pm, first meeting: Nov. 4 |
| Location: |
The seminar will be fully virtual/online and held on Zoom. See ILIAS for Zoom link. |
| Organizer: | Frank Hutter , Noor Awad , Fabio Ferreira |
| Web page: | ILIAS , HISinOne |
Organizational
- we will meet every Wednesday, 12:30-2pm, starting Nov 4
- the seminar will be fully virtual and carried out with Zoom
- we will announce the Zoom meeting link on ILIAS
- link to ILIAS course
- link to HisInOne course
- list of papers released at end of the kick-off meeting
General
Welcome to the Automated Machine Learning Seminar webpage. The seminar language will be in English and it will be comprises of papers from three areas: learning to learn (L2L), hyperparameter optimization (HPO), and neural architecture search (NAS). We will start on Wednesday, Nov. 4 at 12:30-2pm on Zoom. Check out the ILIAS course (link above) for the Zoom link. The papers discussed in this seminar will be announced at the end of the kick-off meeting. We will assume all participants have understood the fundamental concepts presented in the AutoML course .Procedure
We will meet weekly to discuss research papers from a yet to be released list of papers. Every week, two students present each one paper and will lead the succeeding discussion. All other participants read the paper and submit 3 questions due the Monday before via ILIAS. The presenter will be given access to the questions before the presentation. The discussion after the presentation will be based but not limited to the submitted questions. During the discussion, all participants are asked to be involved as much as possible. We will discuss the paper, its merits, limitations, etc.. In the end, the participants are asked to provide constructive feedback to the presenter. The feedback will not be considered for grading. The final grade takes the oral presentation, the questions submitted and class participation into account. Besides the seminar topic, you will learn several skills necessary not only in academia:- read and understand research papers
- assessing the strengths and weaknesses
- oral presentation in front of your peers
- discussion with your peers
- high level summary of research with which you are not intimately familiar.
Schedule
The schedule is yet to be defined.Material
All relevant material will be uploaded to the ILIAS course.Contact
For questions, please email the organizers.
| Course type: | Lecture + Exercise |
| Time: |
Wednesday, 14:15 - 15:45, first meeting: Nov. 4
Exception: Week 8, Thursday, 14:15 - 15:45, Jan. 7 |
| Location: |
The course will be fully virtual/online. Weekly flipped classroom sessions will be held on Zoom. See ILIAS for Zoom link. |
| Organizers: | Frank Hutter , Abhinav Valada , Joschka Bödecker , Steven Adriaensen , Tim Frederic Runge |
| Web page: | ILIAS , HISinOne (lecture) , HISinOne (exercises, please register for both!) |
Foundations of Deep Learning
Deep learning is one of the fastest growing and most exciting fields. This course will provide you with a clear understanding of the fundamentals of deep learning including the foundations to neural network architectures and learning techniques, and everything in between.Course Overview
The course will be taught in english and will follow a flipped classroom approach. Every week there will be:- a video lecture
- an exercise sheet
- a flipped classroom session (virtual/online, Wednesdays 14:15-15:45) At the end, there will be a written exam (online ILIAS test). Exercises must be completed in groups and must be submitted a week (+ 1 day) after their release. Your submissions will be graded and you will receive weekly feedback. Your final grade will be solely based on a written examination, however a passing grade for the exercises is a prerequisite for passing the course. Online course: All material will be made available online and course participation will not require in-person presence.
Online exam: The exam is a test you complete on ILIAS and does not require in-person presence.
Course Schedule
The following are the dates for the release of video lectures: 04.11.20 - Week 1: Overview of Deep Learning11.11.20 - Week 2: From Logistic Regression to MLPs
18.11.20 - Week 3: Backpropagation
25.11.20 - Week 4: Optimization
02.12.20 - Week 5: Regularization
09.12.20 - Week 6: Convolutional Neural Networks (CNNs)
16.12.20 - Week 7: Recurrent Neural Networks (RNNs)
06.01.21 - Week 8: Practical Methodology & Architectures (flipped classroom on Thursday 07.01!)
13.01.21 - Week 9: Hyperparameter Optimization
20.01.21 - Week 10: Neural Architecture Search
27.01.21 - Week 11: Auto-Encoders, Variational Auto-Encoders, GANs
03.02.21 - Week 12: Uncertainty in Deep Learning On the same day, there is a flipped classroom session about the material released the week before. An exception is Week 8, where the flipped classroom session on RNNs will take place on Thursday 07.01 (14:15-15:45) instead of Wednesday 06.01 (holiday). We will be using Zoom and the meeting link can be found on ILIAS in the Flipped Classroom folder. In the first session (on 04.11.20) you will instead get additional information about the course and get the opportunity to ask general questions (and form groups!) While there is no need to prepare for this first session, we encourage you to already think about forming teams. The first exercise on 'Week 1: Overview of Deep Learning' is due on 12.11.20 at 23:59. The last flipped classroom session is held on 10.02.21.
Competition Results
This semester, we organised an optional student competition. In this challenge, students were to train a model to perform class prediction on a flower dataset (more info here ).There were two tracks:
- Fast-track (models with less than 100k parameters)
- Large-track (models with less than 25M parameters)
The winners per track were determined based on the accuracy of the submitted models on a hidden test set.
The Fast-track podium:
- 1st place: Giulio Neusch-Frediani (accuracy: 76.25%)
- 2nd place: Guri Zabergja, Jeta Bekteshi, Albanot Makolli (accuracy: 72.75%)
- 3rd place: Florian Diederichs, Lukas Koenig, Nina Pant (accuracy: 71.75%)
The Large-track podium:
- 1st place: Guri Zabergja, Jeta Bekteshi, Albanot Makolli (accuracy: 98.5%)
- 2nd place: Florian Diederichs, Lukas Koenig, Nina Pant (accuracy: 97.0%)
- 3rd place: Samuel Boehm, Narges Dastanpour, Vytautas Jankauskas (accuracy: 95.0%)
Congratulations!
Questions?
If you have a question, please post it in the ILIAS forum (so everyone can benefit from the answer).Alternatively, you can also email dl-orga-ws20@cs.uni-freiburg.de
| Course type: | Oberseminar: Reading Group (no ECTS) -- Automated Machine Learning |
| Location: | online |
| Web page: | Reading Group |
| Course type: | B.Sc./M.Sc. projects |
| Kickoff meeting: | TBA |
| Location: | online only |
| Web page: |
Open Projects |
| Course type: | Lab Course |
| Time: | Wednesday, 14:00-16:00 |
| Location: | online only |
| Organizer: | Fabio Ferreira , Jörg Franke , Arbër Zela , Frank Hutter (Frank is on parental leave this summer, so please contact the other organizers for questions) |
| Web page: | Website |
| Course type: | Flipped-classroom Lecture + Exercise |
| Time: | Tuesday 14:00 - 15:30 |
| Location: | online only |
| Organizer: | André Biedenkapp , Arbër Zela , Katharina Eggensperger , Matthias Feurer , Frank Hutter (Frank is on parental leave this summer, so please contact the other organizers for questions) |
| Web page: |
AutoML |
| Course type | Oberseminar: Reading Group (no ECTS) -- Automated Machine Learning |
| Time: | Thursdays, 10:45 |
| Location: | Kitchen, Building 074 |
| Web page: |
Reading Group |
| Course type: | B.Sc./M.Sc. projects |
| Kickoff meeting: | TBA |
| Location: | Kitchen, Building 074 |
| Web page: |
Open Projects |
| Course type: | Seminar |
| Time: | Thursday, 12:30-14:00 (first meeting, October 24th, 12:15-13:45) |
| Location: | Building 101, Room 01 018 |
| Organizers: | Frank Hutter , Katharina Eggensperger , Matthias Feurer , Noor Awad , Arbër Zela |
| Web page: | , HisInOne |
Seminar on Bayesian Optimization
The seminar language will be English (even if everyone is German-spoken, to practice presentation skills in English). *First meeting:- 24th October, 12:15-13:45, SR 01 018 (Building 101).
- Every Thursday, 12:30-14:00, SR 01 018 (Building 101).
Background
Bayesian optimization is a popular method for blackbox function optimization. Blackbox function are functions for which no assumptions are made, which means that neither the derivatives nor the smoothness are known. Furthermore, function evaluations might be noisy and are typically assumed to be expensive. Finally, the only knowledge about blackbox functions is the call signature which allows one to query the function for different input values (and observe the outcome). These properties make Bayesian optimization an ideal method for hyperparameter optimization. In this seminar we will read papers on both the foundations of Bayesian optimization and recent research aiming to apply Bayesian optimization to state-of-the-art deep learning models.Schedule
##14.11
## 1. Practical Bayesian Optimization of Machine Learning Algorithms - Spearmint 2. A Tutorial on Bayesian Optimization - with focus on the knowledge gradient and entropy search ##21.11
## 1. Scalable Bayesian Optimization Using Deep Neural Networks 2. Automating Bayesian optimization with Bayesian optimization ##28.11
## 1. Portfolio Allocation for Bayesian Optimization 2. Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization ##05.12
## 1. Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets 2. BOHB: Robust and Efficient Hyperparameter Optimization at Scale ##09.01
## 1. Neural Architecture Search with Bayesian Optimization and Optimal Transport 2. Scalable Hyperparameter Transfer Learning ##16.01
## 1. A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations 2. High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups ##23.01
## 1. Constrained Bayesian Optimization with Noisy Experiments 2. Hyperparameter Importance Across Datasets ##13.02
## 1. Scalable Meta-Learning for Bayesian Optimization using Ranking-Weighted Gaussian Process Ensembles 2. Scalable Global Optimization via Local Bayesian OptimizationRequirements
- Machine Learning
- Statistical Pattern Recognition
- Automated Machine Learning would be good
Material
Further information
For questions, please send an email to one of the organizers: Arbër Zela
| Course type: | Lecture + Exercise |
| Time: | Lecture: Monday 14:15 - 15:45; Exercise: Thursday 10:00 - 11:30 |
| Location: | Lecture: Building 101, HS 00 026 (μ-SAAL); Exercise: Building 082, HS 00 006 (KinoHörsaal) |
| Organizers: | Frank Hutter , Joschka Bödecker , Abhinav Valada , Arber Zela , Raghu Rajan , Jörg Franke , Andreas Sälinger |
| Web page: | , ILIAS |
Foundations of Deep Learning
Deep learning is one of the fastest growing and exciting fields. This course will provide you with a clear understanding of the fundamentals of deep learning including the foundations to neural network architectures and learning techniques, and everything in between.Course Overview
The following are the dates for the in-class discussions (The video lectures will be released on the Tuesday the week earlier): 21.10.19 - Week 1: Overview of Deep Learning04.11.19 - Week 2: From Logistic Regression to MLPs
11.11.19 - Week 3: Backpropagation
18.11.19 - Week 4: Optimization
25.11.19 - Week 5: Regularization
02.12.19 - Week 6: Convolutional Neural Networks (CNNs)
09.12.19 - Week 7: Recurrent Neural Networks (RNNs)
16.12.19 - Week 8: Practical Methodology & Architectures
13.01.19 - Week 9: Hyperparameter Optimization & Neural Architecture Search
20.01.20 - Week 10: Uncertainty in Deep Learning
27.01.20 - Week 11: Auto-Encoders, Variational Auto-Encoders, GANs
03.02.20 - Week 12: Group Presentations; Project Kickoff
The course will be taught in english and we will follow a flipped classroom approach.
| Course type: | Oberseminar: Reading Group (no ECTS) -- Automated Machine Learning |
| Time: | Thursdays, 10:45 |
| Location: | Kitchen, Building 074 |
| Web page: |
Reading Group |
| Course type: | Lab Course |
| Time: | Wednesday, 14:00-16:00 |
| Location: | SR-00-019, Building 74 (MST Pool) |
| Organizer: | Frank Hutter , Aaron Klein , Matilde Gargiani , Jörg Franke , Arbër Zela |
| Web page: | Website |
| Course type: | Lecture + Exercise |
| Time: | Monday 14:00 - 16:00 and Friday 14:00 - 16:00 |
| Location: | SR-00-007, Building 106 |
| Organizer: | Marius Lindauer , Frank Hutter , André Biedenkapp , Arbër Zela , Katharina Eggensperger |
| Web page: |
AutoML |
| Course type: | Lecture + Exercise |
| Time: | Monday 16:00 - 18:00 and Thursday 16:00 - 18:00 |
| Location: | Building 101 - HS 00 026 |
| Organizers: | Frank Hutter , Michael Tangermann , Marius Lindauer |
| Web page: | ILIAS |
| Course type: | Oberseminar: Reading Group (no ECTS) -- Machine Learning and Automated Algorithm Design |
| Time: | Thursdays, 10:45 |
| Location: | Kitchen, Building 074 |
| Web page: |
Reading Group |
| Course type: | B.Sc./M.Sc. projects |
| Kickoff meeting: | TBA |
| Location: | Kitchen, Building 074 |
| Web page: |
Open Projects |
| Course type: | Lab Course |
| Time: | Tuesday 14:00 - 16:00 |
| Location: | Building 82, room 00-006 (Kinohörsaal) |
| Organizers: | Aaron Klein , Matilde Gargiani , Frank Hutter , |
| Web page: | Website |
| Course type: | Lecture + Exercise |
| Time: | Lecture: Monday 14:15 (s.t.) - 15:45; Exercise: Tuesday 12:30 - 14:00 |
| Location: | Lecture: Building 106 SR 00-007; Exercise: Building 051 SR 00 006 |
| Organizers: | Marius Lindauer , André Biedenkapp , Frank Hutter |
| Web page: | , ILIAS |
Machine Learning for Automated Algorithm Design
News
- All news, slides and exercise sheets will be online at ILIAS
Background
Automated algorithm design gets more and more important because algorithms get more complex and a developer has to make calls about many aspects, e.g., which subroutine to use or how to set parameters (also known as magic constants). From the perspective of a user, there often exist more than one algorithm to solve a given problem. So, how can we efficiently choose a well performing algorithm? In practice, it is even worse: There exist no single well-performing parameter setting or best algorithm for all kind of possible inputs. Therefore, we have to determine the best settings for new inputs again and again which can be (i) a really time-consuming and tedious task and (ii) a human is often biased by her own experience which is often not optimal. To automatically solve all these problems, automated algorithm design can be used to determine well-performing algorithm parameter settings, to select the best algorithm for a given input, or even to help to develop and implement better algorithms. In our course "ML4AAD", we will discuss all these problems and how to solve them. To this end, we will use a lot machine learning and optimization techniques to solve them effectively. Furthermore, we will focus on hard combinatorial problems and machine learning as exemplary classes of algorithms to apply automated algorithm design in the course. We strongly recommend that you know the foundations of (i) artificial intelligence (AI) and (ii) machine learning (ML) in order to attend the course. The participants should have attended at least in one other course for AI and ML in the past. In particular, the lectures are partitioned in several parts:- Algorithm selection
- Algorithm design for solving combinatorial problems and machine learning
- Empirical evaluation of algorithms
- Hyperparameter optimization
- Neural architecture search
- Algorithm configuration
- Combinations of algorithm configuration and algorithm selection
- Algorithm analysis
Dynamics
__The course will be in English.__ We will meet weekly for a lecture and an exercise. Roughly every week, there will be a new exercise sheet. Most exercises will be practical and involve teamwork (teams of 2 students!) so that you learn how to apply automatic algorithm design in practice. The exercises are no requirement for the exam (however highly recommended) but to get a grade in the end, you have to pass the exercises.- Lecture: Monday 14:15 (s.t.) - 15:45 in Building 106 SR 00 007
- Exercise: Tuesday 12:30 (s.t.) - 14:00 in Building 051 SR 00 006
Exam
In the end, everyone (no team work!) has to implement a larger project which is the base of the final oral exam. In the first 15 minutes of the oral exam, you have to present your project and in the second 15 minutes, we will ask you to answer questions about further course material.
| Course type: | Lecture + Exercise |
| Time: | Lecture: Monday 12:15 - 13:45 |
| Location: | Building 82, room 00-006 (Kinohörsaal) |
| Organizers: | Frank Hutter , Raghu Rajan (exercises) |
| Web page: | ILIAS |
| Course type: | Oberseminar: Reading Group (no ECTS) -- Machine Learning and Automated Algorithm Design |
| Time: | Mondays, 10:45 |
| Location: | Kitchen, Building 074 |
| Web page: |
Reading Group |
| Course type: | Lab Course |
| Time: | Monday, 10:00-12:00 |
| Location: | SR-00-019, Building 74 (MST Pool) |
| Organizer: | Frank Hutter , Marius Lindauer |
| Web page: |
Machine Learning for Automated Algorithm Design (Lab Course) |
| ILIAS |
| Course type: | Seminar |
| Time: | Friday, 14:00-16:00 |
| Location: | SR-01-016, Building 101 |
| Organizer: | Frank Hutter , Marius Lindauer |
| Web page: |
Advanced Topics in Automated Algorithm Design |
| ILIAS |
| Course type: | Seminar |
| Time: | Tuesday, 14:00-16:00 The seminar will start on April 24th |
| Location: | SR-00-007, Building 106 |
| Organizer: | Frank Hutter , Matthias Feurer , Aaron Klein , Mathilde Gargiani , |
| Web page: |
Advanced Deep Learning |
| Course type: | Lecture + Exercise |
| Time: | Tuesday 12:15 - 13.45 and Thursday 12:15 - 13:45 |
| Location: | Building 101 - HS 00 036 |
| Organizers: | Michael Tangermann , Frank Hutter |
| Web page: | ILIAS |
| Course type: | Oberseminar: Reading Group (no ECTS) -- Machine Learning and Automated Algorithm Design |
| Time: | Fridays, 10:45 |
| Location: | Kitchen, Building 074 |
| Web page: |
Reading Group |
| Course type: | B.Sc./M.Sc. projects |
| Kickoff meeting: | TBA |
| Location: | Kitchen, Building 074 |
| Web page: |
Open Projects |
| Course type: | Lab Course |
| Time: | Monday 14:00 - 16:00 |
| Location: | Kinohörsaal, Building 082 |
| Organizers: | Aaron Klein , Frank Hutter , Joschka Bödecker |
| Web page: | Machine Learning and Computer Vision Track |
| Course type: | Lecture + Exercise |
| Time: | Lecture: Friday 14:00 - 15:30 |
| Location: | Building 106 SR 00-007 (Friday) |
| Organizers: | Frank Hutter , Marius Lindauer , Joschka Bödecker , Gabriel Kalweit |
| Web page: | , ILIAS |
Reinforcement Learning
News
- All news and exercise sheets will be online at ILAS
Background
The lecture deals with methods of Reinforcement Learning that constitute an important class of machine learning algorithms. Starting with the formalization of problems as Markov decision processes, a variety of Reinforcement Learning methods are introduced and discussed in-depth. The connection to practice-oriented problems is established throughout the lecture based on many examples.Dynamics
__The course will be in English.__ The course will be held in a flipped classroom manner. The students have to watch a lecture video each week at home and we will meet weekly to answer questions, discuss the new content and start to solve exercises. Roughly every week, there will be a new exercise sheet. Most exercises will be practical (teams of 2-3 students!) so that you learn how to apply reinforcement learning in practice. You must get 50% points from the exercise to participate in the final exam (see below).- Building: 106; Room: SR 00 007
- Session: Friday 14:00 - 15:30
Exam
In the end, everyone has to implement a larger project which is the base of the final oral exam. In the first 15 minutes of the oral exam, you have to present your project and in the second 15 minutes, we will ask you to answer questions about further course material.
| Course type: | Lecture + Exercise |
| Time: | Lecture/Exercise: Tuesday 12:30 - 14:00 |
| Location: | Building 051 SR 03-026 |
| Organizers: | Frank Hutter , Stefan Falkner |
| Web page: | ILIAS |
| Course type: | Lecture + Exercise |
| Time: | Lecture: Monday 12:20 - 13:50; Lecture/Exercise: Wednesday 12:20 - 13:50 |
| Location: | Building 106 SR 00-007 |
| Organizers: | Frank Hutter , Marius Lindauer , André Biedenkapp |
| Web page: | , ILIAS |
Machine Learning for Automated Algorithm Design
News
- The first session will be at the Oct 18th. We will not meet at the Oct 16th.
- All news and exercise sheets will be online at ILAS
Background
Automatic algorithm design gets more and more important because algorithms get more complex and a developer has to make calls about many aspects, e.g., which subroutine to use or how to set parameters (also known as magic constants). From the perspective of a user, there often exist more than one algorithm to solve a given problem. So, how to choose a well performing algorithm? In practice, it is even worse: There exist no single well-performing parameter setting or best algorithm for all kind of possible inputs. Therefore, we have to determine the best settings for new inputs again and again which can be (i) a really time-consuming and annoying task and (ii) a human is often biased by her own experience which is often not optimal. To automatically solve all these problems, automatic algorithm design can be used to determine well-performing algorithm parameter settings, to select the best algorithm for a given input, or even to help to develop and implement better algorithms. In our course "ML4AAD", we will discuss all these problems and how to solve them. To this end, we will use a lot machine learning and optimization techniques to solve them effectively. Furthermore, we will focus on hard combinatorial problems as an example class of algorithms to apply automatic algorithm design in the course. We strongly recommend that you know the foundations of (i) artificial intelligence (AI) and (ii) machine learning (ML) in order to attend the course. The participants should have attended at least in one other course for AI and ML in the past. In particular, the lectures are partitioned in 8 modules:- Introduction to NP-Hard problems
- Methods for solving combinatorial problems
- Empirical evaluation of algorithms
- Statistical models of the empirical hardness of NP-hard problems
- Algorithm selection
- Algorithm configuration
- Meta-learning
- Hyperparameter optimization
Dynamics
__The course will be in English.__ We will meet weekly for a lecture and an exercise. Roughly every second week, there will be a new exercise sheet. Most exercises will be practical (teams of 2 students!) so that you learn how to apply automatic algorithm design in practice. You must get 50% points from the exercise to participate in the final exam (see below).- Building: 106; Room: SR 00 007
- Lecture: Monday 12:20 - 13:50
- Exercise: Wednesday 12:20 - 13:50
Exam
In the end, everyone (no team work!) has to implement a larger project which is the base of the final oral exam. In the first 15 minutes of the oral exam, you have to present your project and in the second 15 minutes, we will ask you to answer questions about further course material.
| Course type: | Oberseminar: Reading Group (no ECTS) -- Machine Learning and Automated Algorithm Design |
| Time: | Mondays, 10:45 |
| Location: | Kitchen, Building 074 |
| Web page: |
Reading Group |
| Course type: | B.Sc./M.Sc. projects |
| Kickoff meeting: | Wednesday, May 3rd 2017 at 1pm (s.t.) |
| Location: | Kitchen, Building 074 |
| Web page: |
Open Projects |
| Course type: | Lecture + Exercise |
| Time: | Lecture: Monday 12:30 - 14.00; Exercise: Thursday 12:30 - 14:00 |
| Location: | Building 101 - HS 00 036 SCHICK - SAAL |
| Organizers: | Michael Tangermann |
| Web page: | ILIAS |
| Course type: | Seminar |
| Time: | Friday, 14:00-15:30 |
| Location: | SR-00-007, Building 106 |
| Organizer: | Frank Hutter , Stefan Falkner , Marius Lindauer |
| Web page: |
Advanced Topics in Deep Learning |
| ILIAS |
| Course type: | Oberseminar: Reading Group (no ECTS) -- Machine Learning and Automated Algorithm Design |
| Time: | Wednesdays, 13:15 |
| Location: | Kitchen, Building 074 |
| Web page: |
Reading Group |
| Course type: | B.Sc./M.Sc. projects |
| Kickoff meeting: | Monday, October 24th 2016 |
| Location: | Kitchen, Building 074 |
| Web page: |
Open Projects |
| Course type: | Lecture + Exercise |
| Time: | Friday, 14:00-16:00. Beginning: Friday, October 21, 2016 |
| Location: | Kinohörsaal, Building 082 |
| ECTS Credits: | 4 or 6 |
| Requirements: | Bachelor or Master students. Fundamental programming skills in Python are recommended. |
| Organizer: | Ilya Loshchilov , Frank Hutter |
| Web page: |
Deep Learning Course, Automated Algorithm Design Section |
| Course type: | Seminar |
| Time: | Monday, 10:15-11:45 |
| Location: | SR-00 031, Building 51 |
| Organizer: | Frank Hutter , Aaron Klein , Stefan Falkner |
| Web page: |
Deep Learning and Hyperparameter Optimization |
| ILIAS |
| Course type: | Lecture + Exercise |
| Time: | Tuesday 12.30 - 14.00, Thursday 12.30 - 13.15 (Lecture); Thursday 13.15 - 14.00 (Exercise) |
| Location: | Building 101 - HS 00 036 SCHICK - SAAL |
| Organizers: | Michael Tangermann , Frank Hutter |
| Web page: | Ilias |
| Daphne | |
| Setup |
| Course type: | Lab course (Praktikum) |
| Time: | Monday 14.15-15.45 |
| Location: | TBA |
| Organizers: | Frank Hutter |
| Web page: | Lab Course Automated Machine Learning applied to Machine Learning Challenges |
| Course type: | Lecture + Exercise |
| Time: | Tuesday 08:15 - 09:45 (Lecture); Thursday 08:30 - 10:00 (Exercise) |
| Location: | Building: 106; Room: SR 00 007 |
| Organizers: | Frank Hutter |
| Web page: | Machine Learning and Optimization for Algorithm Design |
| Course type: | Praktikum |
| Time: | Mondays, 14:15-15:45 (first meeting: April 20) |
| Location: | Building 074 - 00-023 |
| Organizer: | Frank Hutter |
| Web page: | Praktikum Bayesian Optimization |
| Course type: | Seminar |
| Time: | Wednesday, 10:00-12:00 (first meeting: 22th October) |
| Location: | Building 051 - HS 03 026 |
| Organizers: | Frank Hutter |
| Web page: | AI for Automated Algorithm Design |
| Course type: | Lecture |
| Time: | Tuesdays, 16:00-17:30 and Thursdays, 08:15-09:45 |
| Location: | Building 051 - 00-031 |
| Organizers: | Martin Riedmiller , Frank Hutter , Manuel Blum |
| Web page: | Machine Learning |
| Slides: | Hyperparameter optimization slides: ML.pdf , ML2.pdf , ML3.pdf |
| HPOlib google group: | click here |
| Course type: | Seminar |
| Time: | Thursdays, 14:15-15:45 (first meeting: May 8) |
| Location: | Building 052 - HS 02-017 |
| Organizers: | Frank Hutter , Marius Lindauer |
| Web page: | Automated Parameter Tuning and Algorithm Configuration |
| Course type: | Seminar |
| Time: | Fridays, 12:15-13:45 |
| Location: | S1-018 |
| Organizer: | Frank Hutter |
| Web page: | Automated Parameter Tuning and Algorithm Configuration |