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Learning log. Two students share and compare their learning logs. Learning Logs are a personalized learning resource for children. In the learning logs, the children record their responses to learning challenges set by their teachers. Each log is a unique record of the child's thinking and learning. The logs are usually a visually oriented ...
In computational learning theory, probably approximately correct ( PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant. [1] In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions.
In machine learning applications where logistic regression is used for binary classification, the MLE minimises the cross-entropy loss function. Logistic regression is an important machine learning algorithm. The goal is to model the probability of a random variable being 0 or 1 given experimental data.
The perplexity is the exponentiation of the entropy, a more straightforward quantity. Entropy measures the expected or "average" number of bits required to encode the outcome of the random variable using an optimal variable-length code. It can also be regarded as the expected information gain from learning the outcome of the random variable ...
Rademacher complexity. In computational learning theory ( machine learning and theory of computation ), Rademacher complexity, named after Hans Rademacher, measures richness of a class of sets with respect to a probability distribution. The concept can also be extended to real valued functions.
Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression , [2] : 718 which takes linear time , rather than by expensive iterative approximation as used for many other ...
Supervised learning. Supervised learning ( SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. [1]
Team-based learning ( TBL) is a collaborative learning and teaching strategy [1] that enables people to follow a structured process to enhance student engagement and the quality of student or trainee learning. [2] The term and concept was first popularized by Larry Michaelsen, the central figure in the development of the TBL method while at ...
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