Python is an interpreted, object-oriented programming language. Despite it's popularity, it's often accused of being slow. In this course you will learn how to optimize the performance of your Python code. You will learn various tricks to reduce execution time. A lot of people have different definitions of performance.

With the rapid pace of innovation continually disrupting business models, and in many cases entire industries, how will online learning keep up to provide the relevant courseware for today's and tomorrow's workforce? This will be essential for economic growth and to support a thriving, college-educated workforce that's equipped with the very latest knowledge, ideas and technology. In the future, I believe that institutions at the forefront of online education will be recognized via several capabilities which will have digitally transformed today's EdTech market. They will include a powerful combination of omni-channel learning pathways, cognitive courseware, virtual counselors and AI-enabled course development and grading. These innovations, underpinned by artificial intelligence (AI), will help to provide students the ultimate choice in their courseware – including up-to-the-minute courses on high-interest/high-growth subject matter – as well as highly-innovative digital services that support them every step of the way to help maximize their success and personal objectives.

RMIT Vietnam, an entity of RMIT University, has campuses in Ho Chi Minh City and Hanoi since 2001. We provide internationally recognised, high-quality, education and professional training for students, clients and the community, and assist in the development of human resources capability in Vietnam and the region by hosting students from Australia and many other countries. The School of Science, Engineering & Technology at RMIT Vietnam is associated with the STEM College of RMIT University Melbourne, delivering the College's award programs and developing research in the field(s) of science, engineering and health. Programs currently taught on the Vietnam campus include the Bachelor of Information Technology, Bachelor of Engineering (Electrical and Electronics), Bachelor of Engineering (Software Engineering), Bachelor of Engineering (Robotics & Mechatronics), Bachelor of Applied Science (Aviation), Bachelor of Applied Science (Psychology), Bachelor of Science (Food Technology and Nutrition, Master of Artificial Intelligence and the Doctor of Philosophy (Engineering) (Electrical and Electronic Engineering). As a senior RMIT Vietnam staff member in Master of AI, the Program Manager (PM) will provide leadership of the Artificial Intelligence discipline across education, research, engagement and administration.

If you're here, you already know the truth: Machine Learning is the future of everything. In the coming years, there won't be a single industry in the world untouched by Machine Learning. A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change. You probably already use apps many times each day that rely upon Machine Learning techniques. So why stay in the dark any longer?

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.