Given only positive examples and unlabeled examples (from both positive and negative classes), we might hope nevertheless to estimate an accurate positive-versus-negative classifier. Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE)---determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning---given such an estimate, learning the desired positive-versus-negative classifier. Unfortunately, classical methods for both problems break down in high-dimensional settings. Meanwhile, recently proposed heuristics lack theoretical coherence and depend precariously on hyperparameter tuning. In this paper, we propose two simple techniques: Best Bin Estimation (BBE) (for MPE); and Conditional Value Ignoring Risk (CVIR), a simple objective for PU-learning.

Given only positive examples and unlabeled examples (from both positive and negative classes), we might hope nevertheless to estimate an accurate positive-versus-negative classifier. Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE)---determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning---given such an estimate, learning the desired positive-versus-negative classifier. Unfortunately, classical methods for both problems break down in high-dimensional settings. Meanwhile, recently proposed heuristics lack theoretical coherence and depend precariously on hyperparameter tuning. In this paper, we propose two simple techniques: Best Bin Estimation (BBE) (for MPE); and Conditional Value Ignoring Risk (CVIR), a simple objective for PU-learning.

Before we get to the books you've chosen, can you give us a brief overview of what AI is? Artificial intelligence (AI) is the ability of computers and other machines to perform tasks that would normally require human intelligence, such as understanding language, recognizing images, making decisions, and solving problems. There are many different types of AI, including narrow or weak AI, which is designed to perform a specific task, and general or strong AI, which is designed to be capable of performing any intellectual task that a human can. One of the main goals of AI research is to create systems that can perform tasks without explicit instructions. To achieve this, AI systems are often designed to learn from data, using techniques from machine learning. Machine learning is a type of AI that allows systems to improve their performance on a particular task over time by learning from data, rather than being explicitly programmed. AI has the potential to revolutionize many industries and change the way we live and work. Some possible applications of AI include autonomous vehicles, medical diagnosis, language translation, and financial trading.

Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observations conveyed through natural language. Such observations typically include instructions that, in a reinforcement learning (RL) setting, can directly or indirectly guide a player towards completing reward-worthy tasks. In this work, we study the ability of RL agents to follow such instructions. We conduct experiments that show that the performance of state-of-the-art text-based game agents is largely unaffected by the presence or absence of such instructions, and that these agents are typically unable to execute tasks to completion. To further study and address the task of instruction following, we equip RL agents with an internal structured representation of natural language instructions in the form of Linear Temporal Logic (LTL), a formal language that is increasingly used for temporally extended reward specification in RL. Our framework both supports and highlights the benefit of understanding the temporal semantics of instructions and in measuring progress towards achievement of such a temporally extended behaviour. Experiments with 500+ games in TextWorld demonstrate the superior performance of our approach.

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Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts.

At the end of the course, the student is able to apply the knowledge acquired in Languages and algorithms for Artificial Intelligence in order to carry out autonomously a project focusing on a topic agreed upon with the teacher. The contents of the project will be agreed with the teacher in charge of the course.

AI is a computer science area that studies how to make computers imitate intelligent behaviour, like learning and problem-solving. Different AI books algorithms can solve various problems such as playing chess or recognizing speech. Many companies invested in AI to develop products and services AI.