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Artificial Intelligence
IB Computer Science 2018 Case Study Vocabulary
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Terms in this set (25)
Autonomous
A vehicle navigated and maneuvered by a computer without a need for human control or intervention under a range of driving situations and conditions.
Backpropagation
The method used in artificial neural networks to calculate the error contribution of each neuron after a batch of data (in image recognition, multiple images) is processed. This is used by an enveloping optimization algorithm to adjust the weight of each neuron, completing the learning process for that case.
BigO notation
A mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. In computer science, it's used to classify algorithms according to how their running time or space requirements grow as the input size grows.
Bounding boxes
In digital image processing, it is merely the coordinates of the rectangular border that fully encloses a digital image when it is placed over a page, a canvas, a screen or other similar bidimensional background.
Convolutional neural networks
In machine learning, it is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery. It uses relatively little pre-processing compared to other image classification algorithms. This means that the network learns the filters that in traditional algorithms were hand-engineered. This independence from prior knowledge and human effort in feature design is a major advantage.
Cost function
In artificial neural networks, the function to return a number representing how well the neural network performed to map training examples to correct output.
Deep learning
It is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
Dijkstra's algorithm
An algorithm for finding the shortest paths between nodes in a graph. The algorithm exists in many variants; the original variant found the shortest path between two nodes, but a more common variant fixes a single node as the "source" node and finds shortest paths from the source to all other nodes in the graph, producing a shortest-path tree.
End-to-end learning
The entire process from sensors to motors in a robot or agent consists of only one layered or recurrent neural network without modularization, and the network is trained comprehensively by reinforcement learning.
Feature maps
A function which maps a data vector to feature space.
Filters
It is used for blurring, sharpening, embossing, edge detection, and more. This is accomplished by doing a convolution between a kernel and an image.
Filter stride
The number of pixels which a filter moves across the image.
Greedy algorithm
An algorithmic paradigm that follows the problem solving heuristic of making the locally optimal choice at each stage with the hope of finding a global optimum. In many problems, this does not in general produce an optimal solution, but nonetheless it may yield locally optimal solutions that approximate a global optimal solution in a reasonable time.
Machine learning
A field of computer science that gives computers the ability to learn without being explicitly programmed.
Pooling
A form of non-linear down-sampling. There are several non-linear functions to implement this among which max pooling is the most common. It partitions the input image into a set of non-overlapping rectangles and, for each such sub-region, outputs the maximum.
Multilayer perceptron
A class of feedforward artificial neural network. It consists of at least three layers of nodes. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. It utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish it from a linear perceptron. It can distinguish data that is not linearly separable.
Nearest neighbor algorithm
One of the first algorithms used to determine a solution to the travelling salesman problem. In it, the salesman starts at a random city and repeatedly visits the nearest city until all have been visited. It quickly yields a short tour, but usually not the optimal one.
Overfitting
When a statistical model describes random error or noise instead of the underlying relationship. This occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfitted has poor predictive performance, as it overreacts to minor fluctuations in the training data.
Point clouds
A set of data points in some coordinate system.
Receptive field
The first layer of neurons is composed of all the input neurons; neurons in the next layer will receive connections from some of the input neurons (pixels), but not all, as would be the case in a MLP and in other traditional neural networks. In this way, each successive layer is capable of learning increasingly abstract features of the original image.
Sensor fusion
The combining of sensory data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually.
Society of Automotive Engineers
A U.S.-based, globally active professional association and standards developing organization for engineering professionals in various industries. Principal emphasis is placed on transport industries such as automotive, aerospace, and commercial vehicles.
Shift invariance
The discrete equivalent of a time-invariant system, defined such that if y(n) is the response of the system to x(n), then y(n-k) is the response of the system to x(n-k).
Vehicle-to-vehicle protocol
Communication between two vehicles.
Vehicle-to-infrastructure protocol
Communication between a vehicle and a fixed building.
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