(24th-May-2020) • If e is an example, a point estimate for target feature Y is a prediction of a particular value for Y on e. Let...

(23rd-May-2020) • An abstract definition of supervised learning is as follows. Assume the learner is given the following data: a set of...

(22nd-May-2020) • For cases in which there is a natural interpretation of "between," such as where the prediction is about time or space,...

(21st-May-2020) Given a representation and a bias, the problem of learning can be reduced to one of search. Learning is a search through...

(20th-May-2020) Learning is defined in terms of improving performance based on some measure. To know whether an agent has learned, we...

(19th-May-2020) • For an agent to use its experiences, the experiences must affect the agent's internal representation. Much of machine...

(18th-May-2020) • Virtually any task for which an agent can get data or experiences can be learned. The most commonly studied learning...

(17th-May-2020) • The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if...

(16th-May-2020) • Machine learning, a fundamental concept of AI research since the field's inception, is the study of computer algorithms...

(15th-May-2020) • The following components are part of any learning problem: • task:The behavior or task that is being improved • data:...

(14th-May-2020) Learning is the ability of an agent to improve its behavior based on experience. This could mean the following: The range...

(13th-May-2020) Instead of reasoning explicitly in terms of states, it is almost always much more efficient for an agent solving...

(12th-May-2020) For optimization where the domains are continuous, a local search becomes more complicated because it is not obvious what...

(11th-May-2020) Instead of just having possible worlds satisfy constraints or not, we often have a preference relation over possible...

(10th-May-2020) Stochastic local search is simpler for CSPs that are in the form of propositional satisfiability problems, with Boolean...

(9th-May-2020) • One way to visualize the run time of an algorithm for a particular problem is to use a run-time distribution, which...

(8th-May-2020) The last method maintains no data structure of conflicts; instead it picks a neighbor at random and either rejects or...

(7th-May-2020) The first method is to always select the variable-value pair that makes the best improvement. The naive way of doing this...

(6th-May-2020) Iterative best improvement randomly picks one of the best neighbors of the current assignment. Randomness can also be used...

(5th-May-2020) In iterative best improvement, the neighbor of the current selected node is one that optimizes some evaluation function....