Game development is a highly technical practice that traditionally requires programming skills. This serves as a barrier to entry for would-be developers or those hoping to use games as part of their creative expression. While there have been prior game development tools focused on accessibility, they generally still require programming, or have major limitations in terms of the kinds of games they can make. In this paper we introduce Mechanic Maker, a tool for creating a wide-range of game mechanics without programming. It instead relies on a backend symbolic learning system to synthesize game mechanics from examples. We conducted a user study to evaluate the benefits of the tool for participants with a variety of programming and game development experience. Our results demonstrated that participants' ability to use the tool was unrelated to programming ability. We conclude that tools like ours could help democratize game development, making the practice accessible regardless of programming skills.

The rapid growth in natural language processing (NLP) over the last couple years has generated student interest and excitement in learning more about the field. In this paper, we present two types of students that NLP courses might want to train. First, an "NLP engineer" who is able to flexibly design, build and apply new technologies in NLP for a wide range of tasks. Second, an "NLP scholar" who is able to pose, refine and answer questions in NLP and how it relates to the society, while also learning to effectively communicate these answers to a broader audience. While these two types of skills are not mutually exclusive -- NLP engineers should be able to think critically, and NLP scholars should be able to build systems -- we think that courses can differ in the balance of these skills. As educators at Small Liberal Arts Colleges, the strengths of our students and our institution favors an approach that is better suited to train NLP scholars. In this paper we articulate what kinds of skills an NLP scholar should have, and then adopt a backwards design to propose course components that can aid the acquisition of these skills.

Recently, the potential of large language models (LLMs) has been widely used in assisting programming. However, current research does not explore the artist potential of LLMs in creative coding within artist and AI collaboration. Our work probes the reflection type of artists in the creation process with such collaboration. We compare two common collaboration approaches: invoking the entire program and multiple subtasks. Our findings exhibit artists' different stimulated reflections in two different methods. Our finding also shows the correlation of reflection type with user performance, user satisfaction, and subjective experience in two collaborations through conducting two methods, including experimental data and qualitative interviews. In this sense, our work reveals the artistic potential of LLM in creative coding. Meanwhile, we provide a critical lens of human-AI collaboration from the artists' perspective and expound design suggestions for future work of AI-assisted creative tasks.

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has been robot-centered to develop continual learning algorithms that can quickly learn new information on static datasets. In this paper, we take a human-centered approach to continual learning, to understand how humans teach continual learning robots over the long term and if there are variations in their teaching styles. We conducted an in-person study with 40 participants that interacted with a continual learning robot in 200 sessions. In this between-participant study, we used two different CL models deployed on a Fetch mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. The results also show that although there is a difference in the teaching styles between expert and non-expert users, the style does not have an effect on the performance of the continual learning robot. Finally, our analysis shows that the constrained experimental setups that have been widely used to test most continual learning techniques are not adequate, as real users interact with and teach continual learning robots in a variety of ways. Our code is available at https://github.com/aliayub7/cl_hri.

This course is about the discussion of sampling and exploring data, as well as basic probability theory and Bayes' rule. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. The concepts and techniques you will find in this course will serve as building blocks for the inference and modeling courses in the Specialization.

The future of work is changing our views of jobs for the future. The rapid advancement in AI, ML, automation and algorithmic approach is going to make most of the traditional jobs obsolete. According to a research by the World Economic Forum, approximately 65 percent of today's schoolchildren will end up working in occupations that do not yet exist. The majority of work currently dotting the back-office job landscape will cease to exist in the near future. The emergence of the creator economy and remote working has almost transformed the established paradigms in which people used to work and collaborate.

Get A Solid Understanding of C# & Basic Programming Concepts Learn Unity's API from Absolute Basics Learn Object Oriented Programming Concepts Learn C# Scripting With Practical Examples in Unity Use C# Skills for Building Mobile / Android Games Implement Basic AI Features in Unity Learn Version Controlling with Github, Bitbucket & SourceTree Build Real 2D & 3D Example Games With C# & Unity This Course will Teach You everything that you need to get started with C# scripting in Unity. You will learn step by step from scratch every feature of the C# language as well as how to implement it in Unity's API for building Games. All The Content works fine in Unity 2021 . I have taught C# Scripting to thousands of people on my Youtube Channel: Charger Games. I love teaching complex concepts in a simple way, so even if you have no previous coding experience, no need to worry, I'm gonna teach you everything step by step in the perfect order.

Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

Python is one of the fastest-growing programming languages. It is widely used in various business sectors, such as programming, web development, machine learning, and data science. It is a high-level, object-oriented programming language with built-in data structures and dynamic semantics. Python supports different modules and packages, which allows program modularity and code reuse. The language has become so popular in recent times that aspirants are flocking to learn the language and acquire programming skills.

Learn Python Programming Masterclass, Udemy Free Dicount, This Python For Beginners Course Teaches You The Python Language Fast. Includes Python Online Training With Python 3 4.5 (28,797 ratings), Created by Tim Buchalka, Jean-Paul Roberts, Tim Buchalka's Learn Programming Academy, English, Italian [Auto-generated] What you'll learn Have a fundamental understanding of the Python programming language. Have the skills and understanding of Python to confidently apply for Python programming jobs. Acquire the pre-requisite Python skills to move into specific branches - Machine Learning, Data Science, etc.. Add the Python Object-Oriented Programming (OOP) skills to your résumé. Understand how to create your own Python programs. Learn Python from experienced professional software developers. Understand both Python 2 and Python 3. Preview this Course  - GET COUPON CODE