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11,700 | Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, ... | https://en.wikipedia.org/wiki?curid=233488 |
11,701 | The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. Characterizing the generalization of various learning algorithms is an active ... | https://en.wikipedia.org/wiki?curid=233488 |
11,702 | Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. According to Michael I. Jordan, the ideas of machine learning, from methodological p... | https://en.wikipedia.org/wiki?curid=233488 |
11,703 | Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random forest. | https://en.wikipedia.org/wiki?curid=233488 |
11,704 | Some statisticians have adopted methods from machine learning, leading to a combined field that they call "statistical learning". | https://en.wikipedia.org/wiki?curid=233488 |
11,705 | A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (consi... | https://en.wikipedia.org/wiki?curid=233488 |
11,706 | The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the Probably Approximately Correct Learning (PAC) model. Because training sets are finite and the future is uncertain, learning theory usually does not y... | https://en.wikipedia.org/wiki?curid=233488 |
11,707 | For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the tr... | https://en.wikipedia.org/wiki?curid=233488 |
11,708 | In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of function... | https://en.wikipedia.org/wiki?curid=233488 |
11,709 | Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system: | https://en.wikipedia.org/wiki?curid=233488 |
11,710 | Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In th... | https://en.wikipedia.org/wiki?curid=233488 |
11,711 | Types of supervised-learning algorithms include active learning, classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classificat... | https://en.wikipedia.org/wiki?curid=233488 |
11,712 | Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face ... | https://en.wikipedia.org/wiki?curid=233488 |
11,713 | Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algori... | https://en.wikipedia.org/wiki?curid=233488 |
11,714 | Cluster analysis is the assignment of a set of observations into subsets (called "clusters") so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptio... | https://en.wikipedia.org/wiki?curid=233488 |
11,715 | Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with... | https://en.wikipedia.org/wiki?curid=233488 |
11,716 | In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets. | https://en.wikipedia.org/wiki?curid=233488 |
11,717 | Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, informatio... | https://en.wikipedia.org/wiki?curid=233488 |
11,718 | Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables. In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the dimensionality reduction techniques can be consi... | https://en.wikipedia.org/wiki?curid=233488 |
11,719 | One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). This results in a smaller dimension of data (2D instead of 3D), while keeping all original variables in the model without changing the dat... | https://en.wikipedia.org/wiki?curid=233488 |
11,720 | The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduction techniques make this assumption, leading to the area of manifold learning and manifold regularization. | https://en.wikipedia.org/wiki?curid=233488 |
11,721 | Other approaches have been developed which don't fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system. For example topic modeling, meta-learning. | https://en.wikipedia.org/wiki?curid=233488 |
11,722 | As of 2022, deep learning is the dominant approach for much ongoing work in the field of machine learning. | https://en.wikipedia.org/wiki?curid=233488 |
11,723 | Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named "crossbar adaptive array" (CAA). It is learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about ... | https://en.wikipedia.org/wiki?curid=233488 |
11,724 | The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine: | https://en.wikipedia.org/wiki?curid=233488 |
11,725 | It is a system with only one input, situation, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, on... | https://en.wikipedia.org/wiki?curid=233488 |
11,726 | Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input... | https://en.wikipedia.org/wiki?curid=233488 |
11,727 | Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input d... | https://en.wikipedia.org/wiki?curid=233488 |
11,728 | Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms ai... | https://en.wikipedia.org/wiki?curid=233488 |
11,729 | Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded attempts to algorithmically define specific features. An alt... | https://en.wikipedia.org/wiki?curid=233488 |
11,730 | Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. A popular heuristic method for sparse dictionary learning is the K-SVD al... | https://en.wikipedia.org/wiki?curid=233488 |
11,731 | In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or ... | https://en.wikipedia.org/wiki?curid=233488 |
11,732 | In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object. Many outlier detection methods (in particular, unsupervised a... | https://en.wikipedia.org/wiki?curid=233488 |
11,733 | Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit the least to the remainder of the data set.... | https://en.wikipedia.org/wiki?curid=233488 |
11,734 | Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning, and finally meta-learning (e.g. MAML). | https://en.wikipedia.org/wiki?curid=233488 |
11,735 | Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness". | https://en.wikipedia.org/wiki?curid=233488 |
11,736 | Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively r... | https://en.wikipedia.org/wiki?curid=233488 |
11,737 | Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule formula_1 found in the sales data of a supermar... | https://en.wikipedia.org/wiki?curid=233488 |
11,738 | Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent... | https://en.wikipedia.org/wiki?curid=233488 |
11,739 | Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive ... | https://en.wikipedia.org/wiki?curid=233488 |
11,740 | Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program t... | https://en.wikipedia.org/wiki?curid=233488 |
11,741 | Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. | https://en.wikipedia.org/wiki?curid=233488 |
11,742 | Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. | https://en.wikipedia.org/wiki?curid=233488 |
11,743 | An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receive... | https://en.wikipedia.org/wiki?curid=233488 |
11,744 | The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, mach... | https://en.wikipedia.org/wiki?curid=233488 |
11,745 | Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition. | https://en.wikipedia.org/wiki?curid=233488 |
11,746 | Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree mode... | https://en.wikipedia.org/wiki?curid=233488 |
11,747 | Support-vector machines (SVMs), also known as support-vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new exampl... | https://en.wikipedia.org/wiki?curid=233488 |
11,748 | Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squar... | https://en.wikipedia.org/wiki?curid=233488 |
11,749 | A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases an... | https://en.wikipedia.org/wiki?curid=233488 |
11,750 | A Gaussian process is a stochastic process in which every finite collection of the random variables in the process has a multivariate normal distribution, and it relies on a pre-defined covariance function, or kernel, that models how pairs of points relate to each other depending on their locations. | https://en.wikipedia.org/wiki?curid=233488 |
11,751 | Given a set of observed points, or input–output examples, the distribution of the (unobserved) output of a new point as function of its input data, can be directly computed by looking as the observed points and the covariances between those points and the new, unobserved point. | https://en.wikipedia.org/wiki?curid=233488 |
11,752 | Gaussian processes are popular surrogate models in Bayesian optimization used to do hyperparameter optimization. | https://en.wikipedia.org/wiki?curid=233488 |
11,753 | A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. ... | https://en.wikipedia.org/wiki?curid=233488 |
11,754 | Typically, machine learning models require a high quantity of reliable data in order for the models to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as varied as a c... | https://en.wikipedia.org/wiki?curid=233488 |
11,755 | Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training ... | https://en.wikipedia.org/wiki?curid=233488 |
11,756 | In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration wit... | https://en.wikipedia.org/wiki?curid=233488 |
11,757 | Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resource... | https://en.wikipedia.org/wiki?curid=233488 |
11,758 | In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested. | https://en.wikipedia.org/wiki?curid=233488 |
11,759 | Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary se... | https://en.wikipedia.org/wiki?curid=233488 |
11,760 | Machine learning approaches in particular can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pic... | https://en.wikipedia.org/wiki?curid=233488 |
11,761 | Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a speci... | https://en.wikipedia.org/wiki?curid=233488 |
11,762 | Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. | https://en.wikipedia.org/wiki?curid=233488 |
11,763 | Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers often don't primarily make ... | https://en.wikipedia.org/wiki?curid=233488 |
11,764 | Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification. Machine learning models are often vulnerable to manipulation and/or evasion via adversarial machine learning. | https://en.wikipedia.org/wiki?curid=233488 |
11,765 | Researchers have demonstrated how backdoors can be placed undetectably into classifying (e.g. for categories "spam" and well-visible "not spam" of posts) machine learning models which are often developed and/or trained by third parties. Parties can change the classification of any input, including in cases for which a ... | https://en.wikipedia.org/wiki?curid=233488 |
11,766 | Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-... | https://en.wikipedia.org/wiki?curid=233488 |
11,767 | In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that ... | https://en.wikipedia.org/wiki?curid=233488 |
11,768 | Machine learning poses a host of ethical questions. Systems that are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices. For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a ... | https://en.wikipedia.org/wiki?curid=233488 |
11,769 | AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on the objectivity and logical reasoning. Because human languages contain biases, machines trained on language "corpora" will necessarily also learn these biases. | https://en.wikipedia.org/wiki?curid=233488 |
11,770 | Other forms of ethical challenges, not related to personal biases, are seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ... | https://en.wikipedia.org/wiki?curid=233488 |
11,771 | Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units. By 2019, graphic processing units (GPUs), often with AI-specif... | https://en.wikipedia.org/wiki?curid=233488 |
11,772 | A physical neural network or Neuromorphic computer is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-base... | https://en.wikipedia.org/wiki?curid=233488 |
11,773 | Embedded Machine Learning is a sub-field of machine learning, where the machine learning model is run on embedded systems with limited computing resources such as wearable computers, edge devices and microcontrollers. Running machine learning model in embedded devices removes the need for transferring and storing data ... | https://en.wikipedia.org/wiki?curid=233488 |
11,774 | Apollo 13 (April 1117, 1970) was the seventh crewed mission in the Apollo space program and the third meant to land on the Moon. The craft was launched from Kennedy Space Center on April 11, 1970, but the lunar landing was aborted after an oxygen tank in the service module (SM) failed two days into the mission. The cre... | https://en.wikipedia.org/wiki?curid=1770 |
11,775 | A routine stir of an oxygen tank ignited damaged wire insulation inside it, causing an explosion that vented the contents of both of the SM's oxygen tanks to space. Without oxygen, needed for breathing and for generating electric power, the SM's propulsion and life support systems could not operate. The CM's systems ha... | https://en.wikipedia.org/wiki?curid=1770 |
11,776 | Although the LM was designed to support two men on the lunar surface for two days, Mission Control in Houston improvised new procedures so it could support three men for four days. The crew experienced great hardship, caused by limited power, a chilly and wet cabin and a shortage of potable water. There was a critical ... | https://en.wikipedia.org/wiki?curid=1770 |
11,777 | An investigative review board found fault with preflight testing of the oxygen tank and Teflon being placed inside it. The board recommended changes, including minimizing the use of potentially combustible items inside the tank; this was done for Apollo 14. The story of Apollo 13 has been dramatized several times, most... | https://en.wikipedia.org/wiki?curid=1770 |
11,778 | In 1961, U.S. President John F. Kennedy challenged his nation to land an astronaut on the Moon by the end of the decade, with a safe return to Earth. NASA worked towards this goal incrementally, sending astronauts into space during Project Mercury and Project Gemini, leading up to the Apollo program. The goal was achie... | https://en.wikipedia.org/wiki?curid=1770 |
11,779 | NASA had contracted for fifteen Saturn V rockets to achieve the goal; at the time no one knew how many missions this would require. Since success was obtained in 1969 with the sixth SaturnV on Apollo 11, nine rockets remained available for a hoped-for total of ten landings. After the excitement of Apollo 11, the genera... | https://en.wikipedia.org/wiki?curid=1770 |
11,780 | Even before the first U.S. astronaut entered space in 1961, planning for a centralized facility to communicate with the spacecraft and monitor its performance had begun, for the most part the brainchild of Christopher C. Kraft Jr., who became NASA's first flight director. During John Glenn's Mercury "Friendship 7" flig... | https://en.wikipedia.org/wiki?curid=1770 |
11,781 | In 1965, Houston's Mission Control Center opened, in part designed by Kraft and now named for him. In Mission Control, each flight controller, in addition to monitoring telemetry from the spacecraft, was in communication via voice loop to specialists in a Staff Support Room (or "back room"), who focused on specific spa... | https://en.wikipedia.org/wiki?curid=1770 |
11,782 | Apollo 13 was to be the second H mission, meant to demonstrate precision lunar landings and explore specific sites on the Moon. With Kennedy's goal accomplished by Apollo 11, and Apollo 12 demonstrating that the astronauts could perform a precision landing, mission planners were able to focus on more than just landing ... | https://en.wikipedia.org/wiki?curid=1770 |
11,783 | Apollo 13's mission commander, Jim Lovell, was 42 years old at the time of the spaceflight. He was a graduate of the United States Naval Academy and had been a naval aviator and test pilot before being selected for the second group of astronauts in 1962; he flew with Frank Borman in Gemini 7 in 1965 and Buzz Aldrin in ... | https://en.wikipedia.org/wiki?curid=1770 |
11,784 | Jack Swigert, the command module pilot (CMP), was 38 years old and held a B.S. in mechanical engineering and an M.S. in aerospace science; he had served in the Air Force and in state Air National Guards and was an engineering test pilot before being selected for the fifth group of astronauts in 1966. Fred Haise, the lu... | https://en.wikipedia.org/wiki?curid=1770 |
11,785 | According to the standard Apollo crew rotation, the prime crew for Apollo 13 would have been the backup crew for Apollo 10, with Mercury and Gemini veteran Gordon Cooper in command, Donn F. Eisele as CMP and Edgar Mitchell as LMP. Deke Slayton, NASA's Director of Flight Crew Operations, never intended to rotate Cooper ... | https://en.wikipedia.org/wiki?curid=1770 |
11,786 | Swigert was originally CMP of Apollo 13's backup crew, with John Young as commander and Charles Duke as lunar module pilot. Seven days before launch, Duke contracted rubella from a friend of his son. This exposed both the prime and backup crews, who trained together. Of the five, only Mattingly was not immune through p... | https://en.wikipedia.org/wiki?curid=1770 |
11,787 | For Apollo, a third crew of astronauts, known as the support crew, was designated in addition to the prime and backup crews used on projects Mercury and Gemini. Slayton created the support crews because James McDivitt, who would command Apollo 9, believed that, with preparation going on in facilities across the US, mee... | https://en.wikipedia.org/wiki?curid=1770 |
11,788 | For Apollo 13, flight directors were Gene Kranz, White team (the lead flight director); Glynn Lunney, Black team; Milton Windler, Maroon team and Gerry Griffin, Gold team. The CAPCOMs (the person in Mission Control, during the Apollo program an astronaut, who was responsible for voice communications with the crew) for ... | https://en.wikipedia.org/wiki?curid=1770 |
11,789 | The Apollo 13 mission insignia depicts the Greek god of the Sun, Apollo, with three horses pulling his chariot across the face of the Moon, and the Earth seen in the distance. This is meant to symbolize the Apollo flights bringing the light of knowledge to all people. The mission motto, "Ex luna, scientia" ("From the M... | https://en.wikipedia.org/wiki?curid=1770 |
11,790 | On the patch, the mission number appeared in Roman numerals as Apollo XIII. It did not have to be modified after Swigert replaced Mattingly, as it is one of only two Apollo mission insigniathe other being Apollo 11not to include the names of the crew. It was designed by artist Lumen Martin Winter, who based it on a mur... | https://en.wikipedia.org/wiki?curid=1770 |
11,791 | The mission's motto was in Lovell's mind when he chose the call sign "Aquarius" for the lunar module, taken from Aquarius, the bringer of water. Some in the media erroneously reported that the call sign was taken from a song by that name from the musical "Hair". The command module's call sign, "Odyssey", was chosen not... | https://en.wikipedia.org/wiki?curid=1770 |
11,792 | The Saturn V rocket used to carry Apollo 13 to the Moon was numbered SA-508, and was almost identical to those used on Apollo8 through 12. Including the spacecraft, the rocket weighed in at . The S-IC first stage's engines were rated to generate less total thrust than Apollo 12's, though they remained within specificat... | https://en.wikipedia.org/wiki?curid=1770 |
11,793 | The Apollo 13 spacecraft consisted of Command Module 109 and Service Module 109 (together CSM-109), called "Odyssey", and Lunar Module7 (LM-7), called "Aquarius". Also considered part of the spacecraft was the launch escape system, which would propel the command module (CM) to safety in the event of a problem during li... | https://en.wikipedia.org/wiki?curid=1770 |
11,794 | The LM stages, CM and service module (SM) were received at Kennedy Space Center (KSC) in June 1969; the portions of the Saturn V were received in June and July. Thereafter, testing and assembly proceeded, culminating with the rollout of the launch vehicle, with the spacecraft atop it, on December 15, 1969. Apollo 13 wa... | https://en.wikipedia.org/wiki?curid=1770 |
11,795 | The Apollo 13 prime crew undertook over 1,000 hours of mission-specific training, more than five hours for every hour of the mission's ten-day planned duration. Each member of the prime crew spent over 400 hours in simulators of the CM and (for Lovell and Haise) of the LM at KSC and at Houston, some of which involved t... | https://en.wikipedia.org/wiki?curid=1770 |
11,796 | The astronauts of Apollo 11 had minimal time for geology training, with only six months between crew assignment and launch; higher priorities took much of their time. Apollo 12 saw more such training, including practice in the field, using a CAPCOM and a simulated backroom of scientists, to whom the astronauts had to d... | https://en.wikipedia.org/wiki?curid=1770 |
11,797 | Concerned about how close Apollo 11's LM, "Eagle", had come to running out of propellant during its lunar descent, mission planners decided that beginning with Apollo 13, the CSM would bring the LM to the low orbit from which the landing attempt would commence. This was a change from Apollo 11 and 12, on which the LM m... | https://en.wikipedia.org/wiki?curid=1770 |
11,798 | The plan was to devote the first of the two four-hour lunar surface extravehicular activities (EVAs) to setting up the Apollo Lunar Surface Experiments Package (ALSEP) group of scientific instruments; during the second, Lovell and Haise would investigate Cone crater, near the planned landing site. The two astronauts wo... | https://en.wikipedia.org/wiki?curid=1770 |
11,799 | Apollo 13's designated landing site was near Fra Mauro crater; the Fra Mauro formation was believed to contain much material spattered by the impact that had filled the Imbrium basin early in the Moon's history. Dating it would provide information not only about the Moon, but about the Earth's early history. Such mater... | https://en.wikipedia.org/wiki?curid=1770 |
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