Supervised Learning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit III: Supervised Learning
Generative models are a class of statistical models that generate new data instances. These models are used in unsupervised machine learning to perform tasks.
Supervised Learning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit III: Supervised Learning
A classification algorithm (Classifier) that makes its classification based on a linear predictor function combining a set of weights with the feature vector.
Supervised Learning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit III: Supervised Learning
Regression finds correlations between dependent and independent variables. If the desired output consists of one or more continuous variable, then the task is called as regression.
Supervised Learning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit III: Supervised Learning
Learning is essential for unknown environments, i.e. when designer lacks the 10 me omniscience. Learning simply means incorporating information from the training examples into the system.
Supervised Learning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit III: Supervised Learning
Machine Learning (ML) is a sub-field of Artificial Intelligence (AI) which concerns with developing computational theories of learning and building learning machines.
Probabilistic Reasoning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit II: Probabilistic Reasoning
How to handle uncertain knowledge with example?
Probabilistic Reasoning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit II: Probabilistic Reasoning
The Dempster-Shafer theory is designed to deal to deal with the distinction between uncertainty and ignorance.
Probabilistic Reasoning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit II: Probabilistic Reasoning
A directed network which illustrates the causal dependencies of all the components in the network. A causal relationship exists when one variable in a data set has a direct influence on another variable.
Probabilistic Reasoning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit II: Probabilistic Reasoning
In forward reasoning, reasoning proceeds forward, beginning with factor, chaining through rules and finally establishing the goal.
Probabilistic Reasoning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit II: Probabilistic Reasoning
One common view is that probability theory is essentially numerical, whereas human judgemental reasoning is more "qualitative".
Probabilistic Reasoning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit II: Probabilistic Reasoning
Probability based reasoning is the same as inferring directly from knowledge that can be given a probability rating based on the amount of uncertainty present.
Probabilistic Reasoning - Artificial Intelligence and Machine Learning
Subject and UNIT: Artificial Intelligence and Machine Learning: Unit II: Probabilistic Reasoning
It calculates the probability of each hypotheses, given the data and makes the predictions on that basis.