Artificial intelligence plays a role in nearly everyone's life now, so it only seems fair that everyone should also have the opportunity to learn exactly what it is and how it functions. At least, that's what Helsinki University in Finland thinks. The school is offering the world's first online artificial intelligence course geared towards beginners, as Engadget reports. Not only can anyone with web access enroll, but it's also free. Because the course only takes about 30 hours to complete, it's possible it might help people get to know--and form opinions on--artificial intelligence.

Artificial intelligence plays a role in nearly everyone's life now, so it only seems fair that everyone should also have the opportunity to learn exactly what it is and how it functions. At least, that's what Helsinki University in Finland thinks. The school is offering the world's first online artificial intelligence course geared towards beginners, as Engadget reports. Not only can anyone with web access enroll, but it's also free. Because the course only takes about 30 hours to complete, it's possible it might help people get to know--and form opinions on--artificial intelligence.

K.A.PATON 411 24 Centromere finding: some shape descriptors for small chromosome outlines.

Neural one-unit learning rules for the problem of Independent Component Analysis (ICA) and blind source separation are introduced. In these new algorithms, every ICA neuron develops into a separator that finds one of the independent components. The learning rules use very simple constrained Hebbianjanti-Hebbian learning in which decorrelating feedback may be added. To speed up the convergence of these stochastic gradient descent rules, a novel computationally efficient fixed-point algorithm is introduced. 1 Introduction Independent Component Analysis (ICA) (Comon, 1994; Jutten and Herault, 1991) is a signal processing technique whose goal is to express a set of random variables as linear combinations of statistically independent component variables. The main applications of ICA are in blind source separation, feature extraction, and blind deconvolution.

Neural one-unit learning rules for the problem of Independent Component Analysis (ICA) and blind source separation are introduced. In these new algorithms, every ICA neuron develops into a separator that finds one of the independent components. The learning rules use very simple constrained Hebbianjanti-Hebbian learning in which decorrelating feedback may be added. To speed up the convergence of these stochastic gradient descent rules, a novel computationally efficient fixed-point algorithm is introduced. 1 Introduction Independent Component Analysis (ICA) (Comon, 1994; Jutten and Herault, 1991) is a signal processing technique whose goal is to express a set of random variables as linear combinations of statistically independent component variables. The main applications of ICA are in blind source separation, feature extraction, and blind deconvolution.

Neural one-unit learning rules for the problem of Independent Component Analysis(ICA) and blind source separation are introduced. In these new algorithms, every ICA neuron develops into a separator thatfinds one of the independent components. The learning rules use very simple constrained Hebbianjanti-Hebbian learning in which decorrelating feedback may be added. To speed up the convergence of these stochastic gradient descent rules, a novel computationally efficientfixed-point algorithm is introduced. 1 Introduction Independent Component Analysis (ICA) (Comon, 1994; Jutten and Herault, 1991) is a signal processing technique whose goal is to express a set of random variables aslinear combinations of statistically independent component variables. The main applications of ICA are in blind source separation, feature extraction, and blind deconvolution.