Many of us do not know that there is a proper list of machine learning algorithms. So here in this article, we will see some methods of using these algorithms. Through these Machine learning algorithm, you also get to know more about Artificial intelligence and designing machine learning system. These are the most important Algorithms in Machine Learning. If you are aware of these Algorithms then you can use them well to apply in almost any Data Problem.
Crowdsourcing is an important avenue for collecting machine learning data, but crowdsourcing can go beyond simple data collection by employing the creativity and wisdom of crowd workers. Yet crowd participants are unlikely to be experts in statistics or predictive modeling, and it is not clear how well non-experts can contribute creatively to the process of machine learning. Here we study an end-to-end crowdsourcing algorithm where groups of non-expert workers propose supervised learning problems, rank and categorize those problems, and then provide data to train predictive models on those problems. Problem proposal includes and extends feature engineering because workers propose the entire problem, not only the input features but also the target variable. We show that workers without machine learning experience can collectively construct useful datasets and that predictive models can be learned on these datasets. In our experiments, the problems proposed by workers covered a broad range of topics, from politics and current events to problems capturing health behavior, demographics, and more. Workers also favored questions showing positively correlated relationships, which has interesting implications given many supervised learning methods perform as well with strong negative correlations. Proper instructions are crucial for non-experts, so we also conducted a randomized trial to understand how different instructions may influence the types of problems proposed by workers. In general, shifting the focus of machine learning tasks from designing and training individual predictive models to problem proposal allows crowdsourcers to design requirements for problems of interest and then guide workers towards contributing to the most suitable problems.
This course is about solving advanced mechanics problems. This set of problems is taken from the first volume of the course of theoretical physics by Landau and Lifshitz. I have selected some problems from this book and provided a thorough step-by-step solution in the course; the solutions to these problems are also given in the book but they are usually quite terse, namely not many details are provided. Therefore, what we will do in the course is to first construct the necessary theory to deal with the problems, and then we will solve the problems. Some theory is also discussed while solving the problems themselves.
Rieffel, Eleanor (NASA Ames Research Center) | Venturelli, Davide (NASA Ames Research Center) | Do, Minh (NASA Ames Research Center) | Hen, Itay (University of Southern California) | Frank, Jeremy (NASA Ames Research Center)
There are two complementary ways to evaluate planning algorithms: performance on benchmark problems derived from real applications and analysis of performance on parametrized families of problems with known properties. Prior to this work, few means of generating parametrized families of hard planning problems were known. We generate hard planning problems from the solvable/unsolvable phase transition region of well-studied NP-complete problems that map naturally to navigation and scheduling, aspects common to many planning domains. We observe significant differences between state-of-the-art planners on these problem families, enabling us to gain insight into the relative strengths and weaknesses of these planners. Our results confirm exponential scaling of hardness with problem size, even at very small problem sizes. These families provide complementary test sets exhibiting properties not found in existing benchmarks.
This paper investigates how readily an unsolvable constraint satisfaction problem can be reformulated so that it becomes solvable. We investigate small changes in the definitions of the problemís constraints, changes that alter neither the structure of its constraint graph nor the tightness of its constraints. Our results show that structured and unstructured problems respond differently to such changes, as do easy and difficult problems taken from the same problem class. Several plausible explanations for this behavior are discussed.