MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept
- URL: http://arxiv.org/abs/2401.16291v1
- Date: Mon, 29 Jan 2024 16:50:32 GMT
- Title: MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept
- Authors: Michal Tk\'a\v{c}, Jakub Sieber, Lara Kuhlmann, Matthias Brueggenolte,
Alexandru Rinciog, Michael Henke, Artur M. Schweidtmann, Qinghe Gao,
Maximilian F. Theisen, Radwa El Shawi
- Abstract summary: This paper introduces the MachineLearnAthon format, an innovative didactic concept designed to be inclusive for students of different disciplines.
At the heart of the concept lie ML challenges, which make use of industrial data sets to solve real-world problems.
These cover the entire ML pipeline, promoting data literacy and practical skills, from data preparation, through deployment, to evaluation.
- Score: 34.6229719907685
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Machine Learning (ML) techniques are encountered nowadays across disciplines,
from social sciences, through natural sciences to engineering. The broad
application of ML and the accelerated pace of its evolution lead to an
increasing need for dedicated teaching concepts aimed at making the application
of this technology more reliable and responsible. However, teaching ML is a
daunting task. Aside from the methodological complexity of ML algorithms, both
with respect to theory and implementation, the interdisciplinary and empirical
nature of the field need to be taken into consideration. This paper introduces
the MachineLearnAthon format, an innovative didactic concept designed to be
inclusive for students of different disciplines with heterogeneous levels of
mathematics, programming and domain expertise. At the heart of the concept lie
ML challenges, which make use of industrial data sets to solve real-world
problems. These cover the entire ML pipeline, promoting data literacy and
practical skills, from data preparation, through deployment, to evaluation.
Related papers
- ICE-T: A Multi-Faceted Concept for Teaching Machine Learning [2.9685635948300004]
We take a look at didactic principles that are employed for teaching computer science, define criteria, and evaluate a selection of prominent existing platforms, tools, and games.
We criticize the approach of portraying Machine Learning mostly as a black-box and the resulting missing focus on creating an understanding of data, algorithms, and models.
We present a concept that covers intermodal transfer, computational and explanatory thinking, ICE-T, as an extension of known didactic principles.
arXiv Detail & Related papers (2024-11-08T09:16:05Z) - Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach [0.0]
In recent years, AI researchers and practitioners have introduced principles and guidelines to build systems that make reliable and trustworthy decisions.
In practice, a fundamental challenge arises when the system needs to be operationalized and deployed to evolve and operate in real-life environments continuously.
To address this challenge, Machine Learning Operations (MLOps) have emerged as a potential recipe for standardizing ML solutions in deployment.
arXiv Detail & Related papers (2024-10-28T09:34:08Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - Learning with Limited Samples -- Meta-Learning and Applications to
Communication Systems [46.760568562468606]
Few-shot meta-learning optimize learning algorithms that can efficiently adapt to new tasks quickly.
This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications.
arXiv Detail & Related papers (2022-10-03T17:15:36Z) - Machine Learning Operations (MLOps): Overview, Definition, and
Architecture [0.0]
The paradigm of Machine Learning Operations (MLOps) addresses this issue.
MLOps is still a vague term and its consequences for researchers and professionals are ambiguous.
We provide an aggregated overview of the necessary components, and roles, as well as the associated architecture and principles.
arXiv Detail & Related papers (2022-05-04T19:38:48Z) - Panoramic Learning with A Standardized Machine Learning Formalism [116.34627789412102]
This paper presents a standardized equation of the learning objective, that offers a unifying understanding of diverse ML algorithms.
It also provides guidance for mechanic design of new ML solutions, and serves as a promising vehicle towards panoramic learning with all experiences.
arXiv Detail & Related papers (2021-08-17T17:44:38Z) - Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
We have developed a proven systems engineering approach for machine learning development and deployment.
Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
arXiv Detail & Related papers (2021-01-11T15:54:48Z) - Technology Readiness Levels for AI & ML [79.22051549519989]
Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results.
We propose a proven systems engineering approach for machine learning development and deployment.
arXiv Detail & Related papers (2020-06-21T17:14:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.