In reinforcement learning (RL) with experience replay, experiences stored in a replay buffer influence the RL agent's performance. Information about the influence is valuable for various purposes, including experience cleansing and analysis. One meth...
For more than forty years, Heinemann has delivered leading-edge professional learning on pressing teaching topics of the times. Build teacher confidence with guided learning experiences to ensure …
For more than forty years, Heinemann has delivered leading-edge professional learning on pressing teaching topics of the times. Build teacher confidence with guided learning experiences to ensure …
This paper explores how instructors can leverage generative AI to create personalized learning experiences for students that transform teaching and learning. We present a range of AI-based exercises that enable novel forms of practice and application...
"Spillover" learning is defined as customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. In this paper, we propose a novel, parsimonious and general Bayesian hierarchi...
This study explores the integration of real-world machine learning (ML) projects using human-computer interfaces (HCI) datasets in college-level courses to enhance both teaching and learning experiences. Employing a comprehensive literature review, c...
Upgrade your classroom tools to improve communication and class experiences while encouraging original work with new teaching and learning technology.
This paper concerns imitation learning (IL) (i.e, the problem of learning to mimic expert behaviors from demonstrations) in cooperative multi-agent systems. The learning problem under consideration poses several challenges, characterized by high-dime...
Machine Learning - Fundamental of Python Machine Learning [https://freewebcart.com/machine-learning-fundamental-of-python-machine-learning/](https://freewebcart.com/machine-learning-fundamental-of-py...
In recent years, reinforcement learning has seen interest because of deep Q-Learning, where the model is a convolutional neural network. Deep Q-Learning has shown promising results in games such as Atari and AlphaGo. Instead of learning the entire Q-...
The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning tasks. Here, we introduce a computationally efficient online meta-learni...
In past years model-agnostic meta-learning (MAML) has been one of the most promising approaches in meta-learning. It can be applied to different kinds of problems, e.g., reinforcement learning, but also shows good results on few-shot learning tasks....
Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards. Although supervised imitation learning provides a simple and stable alternative, it requires acc...
Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning (⭐ 4694)
This repository has all my projects on Oil and Gas (and Non Oil & Gas) Machine Learning Topics (⭐ 47)
Bem-vindo ao repositório de aulas em Machine Learning! Este repositório foi criado para fornecer recursos de aprendizado em Machine Learning, abrangendo uma variedade de tópicos essenciais e técnicas práticas. As aulas abordam desde conceitos fundamentais até…
Learning novel concepts while preserving prior knowledge is a long-standing challenge in machine learning. The challenge gets greater when a novel task is given with only a few labeled examples, a problem known as incremental few-shot learning. We pr...
In various approaches to learning, notably in domain adaptation, active learning, learning under covariate shift, semi-supervised learning, learning with concept drift, and the like, one often wants to compare a baseline classifier to one or more adv...
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the
Posing high-quality problems is a critical skill to be possessed by students in learning mathematics. However, it is still limited in literature in answering whether problem posing learning model effective in improving students' learning achievement...