The project brochure and poster provide the most important information about the project's partners, activities, and goals. For more details, please take a look at the first digital presentation. During the second semester of the project, the MACHINA partners collected evidence on workplace requirements regarding ML skills. The project partners then defined six learning units based on analyzing the collected evidence and identifying each unit's knowledge, skills, and competencies. For more details, please download the second digital presentation.

When you get involved in a data science project, you must always take care of basic elements first before starting a project like business objective, domain knowledge, standard data science practices of an organization, and previous experiences while considering the next steps to problem solutions like data source identification, data modeling, data management, and data visualizations. The data science industry already offers a variety of data science workflow frameworks to solve different kinds of data science problems. It is not possible to develop an all-inclusive Data Science Workflow to solve all business problems. In lieu of that, it is important to follow some best-standard data science practices, such as automating data pipelines, planning inferences, and doing a post-mortem at the end of every project to identify any potential improvement areas. You will learn about various standard data science workflows in this article. You will also gain an understanding of the structure of a Data Science Workflow and the considerations that need to be taken into account as you follow the Data Science Workflow.

Mathematical Optimization is getting more and more popular in most quantitative disciplines, such as engineering, management, economics, and operations research. Furthermore, Python is one of the most famous programming languages that is getting more attention nowadays. Therefore, we decided to create a course for mastering the development of optimization problems in the Python environment. Since this course is designed for all levels (from beginner to advanced), we start from the beginning that you need to formulate a problem. Therefore, after finishing this course, you will be able to find and formulate decision variables, objective function, constraints and define your parameters.

Internal cluster validity measures (such as the Calinski-Harabasz, Dunn, or Davies-Bouldin indices) are frequently used for selecting the appropriate number of partitions a dataset should be split into. In this paper we consider what happens if we treat such indices as objective functions in unsupervised learning activities. Is the optimal grouping with regards to, say, the Silhouette index really meaningful? It turns out that many cluster (in)validity indices promote clusterings that match expert knowledge quite poorly. We also introduce a new, well-performing variant of the Dunn index that is built upon OWA operators and the near-neighbour graph so that subspaces of higher density, regardless of their shapes, can be separated from each other better.

Flexible, goal-directed behavior is a fundamental aspect of human life. Based on the free energy minimization principle, the theory of active inference formalizes the generation of such behavior from a computational neuroscience perspective. Based on the theory, we introduce an output-probabilistic, temporally predictive, modular artificial neural network architecture, which processes sensorimotor information, infers behavior-relevant aspects of its world, and invokes highly flexible, goal-directed behavior. We show that our architecture, which is trained end-to-end to minimize an approximation of free energy, develops latent states that can be interpreted as affordance maps. That is, the emerging latent states signal which actions lead to which effects dependent on the local context. In combination with active inference, we show that flexible, goal-directed behavior can be invoked, incorporating the emerging affordance maps. As a result, our simulated agent flexibly steers through continuous spaces, avoids collisions with obstacles, and prefers pathways that lead to the goal with high certainty. Additionally, we show that the learned agent is highly suitable for zero-shot generalization across environments: After training the agent in a handful of fixed environments with obstacles and other terrains affecting its behavior, it performs similarly well in procedurally generated environments containing different amounts of obstacles and terrains of various sizes at different locations.

Progress in artificial intelligence has enabled the creation of AIs that perform tasks previously thought only possible for humans, such as translating languages, driving cars, playing board games at world-champion level, and extracting the structure of proteins. However, each of these AIs has been designed and exhaustively trained for a single task and has the ability to learn only what's needed for that specific task. Recent AIs that produce fluent text, including in conversation with humans, and generate impressive and unique art can give the false impression of a mind at work. But even these are specialized systems that carry out narrowly defined tasks and require massive amounts of training. It still remains a daunting challenge to combine multiple AIs into one that can learn and perform many different tasks, much less pursue the full breadth of tasks performed by humans or leverage the range of experiences available to humans that reduce the amount of data otherwise required to learn how to perform these tasks. The best current AIs in this respect, such as AlphaZero and Gato, can handle a variety of tasks that fit a single mold, like game-playing.

Despite being quite effective in a variety of tasks across industries, deep learning is constantly evolving, proposing new neural network (NN) architectures such as the Spiking Neural Network (SNN). This exciting course introduces you to the next generation of Machine Learning. You would be able to learn about the fundamentals of Spiking Neural Networks and Brain-Computer Interfacing (BCI). This course has the rigour enough to enable you not only to understand BCI but its implementation in spiking neural networks and to apply these concepts to Brain Healthcare (IT) even on edge machines using Tiny ML. TinyML is a field of study in Machine Learning and Embedded Systems that explores the types of models you can run on small, low-powered devices like microcontrollers.

This course will provide a prospect for participants to establish or progress their considerate on the Genetic Algorithms, GANs and Variational Auto- encoders and their implementation in Python framework. This course encompasses algorithm processes, approaches, and application dimensions. Genetic algorithm which reflects the process of natural selection though selection of fittest individuals is explained thoroughly. Further its implementation in Python Library is exhibited step- wise. Similarly, Generative Adversarial Networks, or GANs for short, are introduced as an approach to generative modelling.

You will need a laptop computer with specific software installed prior to the session. When you register for the class, you will receive detailed instructions for software download and installation. R is the one of the most popular machine learning tools in use today. This course focuses on taking concepts in machine learning and applying them in practical ways. Common algorithms such as regression, clustering, and classification are explained, applied, and evaluated using R. Participants will complete exercises to solidify understanding and build skills with the intent of finishing the course with a toolkit that can be used to build R machine learning skills.

In the last two decades, technology has grown progressive and futuristic through several advancements. Artificial intelligence -- AI is perhaps the future of technology. Lately, many programmers and developers show immense interest in adopting AI. In this article, we will be highlighting what artificial intelligence for beginners looks like. You will also learn the basics of Artificial Intelligence which would give you an equitable idea about this emerging technology.