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Discrete choice models are used extensively in many disciplines where it is important to predict human behavior at a disaggregate level. This course is a follow up of the online course “Introduction t

Introduction to Discrete Choice Models

The course introduces the theoretical foundations to choice modeling and describes the steps of operational modeling.

Optimization: principles and algorithms - Linear optimization

Introduction to linear optimization, duality and the simplex algorithm.

Optimization: principles and algorithms - Linear optimization

Introduction to linear optimization, duality and the simplex algorithm.

Optimization: principles and algorithms - Network and discrete optimization

Introduction to network optimization and discrete optimization

Optimization: principles and algorithms - Network and discrete optimization

Introduction to network optimization and discrete optimization

Optimization: principles and algorithms - Unconstrained nonlinear optimization

Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.

Optimization: principles and algorithms - Unconstrained nonlinear optimization

Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.

Digital Signal Processing I

Basic signal processing concepts, Fourier analysis and filters. This module can be used as a starting point or a basic refresher in elementary DSP

Digital Signal Processing II

Adaptive signal processing, A/D and D/A. This module provides the basic tools for adaptive filtering and a solid mathematical framework for sampling and quantization

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