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- Introduction to neural networks, examples of their applications.

- Models of neuron, activation functions, network architectures.

- Single neuron models and learning rules: least-mean squares (LMS) algorithm, basic perceptron, their weaknesses.

- Hebbian learning and principal component analysis (PCA), preprocessing of data.

- Feedforward multilayer perceptron (MLP) networks, backpropagation learning algorithms, their properties and some improvements.

- Advanced optimization algorithms for multilayer perceptron networks: conjugate gradient algorithm, Levenberg-Marquardt algorithm.

- Model assessment and selection: generalization, overlearning, regularization, bias-variance decomposition, validation and cross-validation.

- Radial-basis function (RBF) neural networks and their learning algorithms.

- Support vector machines for classification and nonlinear regression.

- Independent component analysis (ICA): basic principles, criteria, learning algorithms, and some applications.

- Self-organizing maps (SOM) and learning vector quantization (LVQ).

- Processing of temporal information in feedforward networks, simple recurrent network.

The problem sheets and solutions of the exercises are available here. The exercises, their solutions, examination requirements, and lecture slides written in English, as well as the additional lecture material used is copied to the participants as lecture notes via Edita Prima Oy.

The exercises consist of standard exercise problems, and computer problems (for example, running given algorithms in example problems). Their solutions are demonstrated by the course assistant in the exercises. Furthermore, selected demos are presented in context with the exercises at suitable places.

The lectures are given by Prof. Juha Karhunen. He can be met during the lectures, or by email: Juha.Karhunen@tkk.fi (Tel. 09-451 3270, mobile 0400-817 276, Room B327 in Computer Science and Engineering Dept. House).

The exercises are given by the course assistant, M.Sc. Matti Pöllä. He can be met during the exercises, or by email: matti.polla@tkk.fi (Tel. 09-451 5115, room B332 in Computer Science and Engineering Dept. House).

Results of the examination on 11th January 2008 are now on the notice board of the course. They are briefly summarized here.

General information about the course

Lecture 1, B/W version

Lecture 2, B/W version

Lecture 3, B/W version

Lecture 4, B/W version

Lecture 5, B/W version

Lecture 6, B/W version

Lecture 7, B/W version

Lecture 8, B/W version

Lecture 9, B/W version

Lecture 10, B/W version

Lecture 11, B/W version

Lecture 12, B/W version

Otaniemi, January 29, 2008

Prof. Juha Karhunen

You are at: CIS → T-61.5130 Machine Learning and Neural Networks (5 cr)

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