Chengzhi Wu

Chengzhi Wu

Ph.D. in Computer Science

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Neural Style Transfer

Sponsored by ZKM | Center for Art and Media Karlsruhe, 2018

NST Project Description: An interactive installation that uses neural networks for real-time AI-driven style transfer.
🏡 Project Homepage

publications

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teaching

Seminar Image Analysis and Fusion

Seminar, IES, KIT, 2019–2022

This is a Seminar offered for master’s students. The Seminar is offered every summer semester, with me supervising one topic in every session.

Automatic Visual Inspection and Image Processing

Lecture, IES, KIT, 2019–2023

This lecture covers the acquisition, description, processing, and evaluation of image data for the purpose of automatic visual inspection. Various sensors and methods for capturing image-based data, as well as the relevant optical principles, are discussed. The mathematical description of image signals is examined in detail. The necessary system-theoretical methods and relationships are derived and discussed. The second half of the lecture focuses on the various sub-tasks and all major signal processing methods used in digital image processing and analysis.

Pattern Recognition

Lecture, IES, KIT, 2019–2023

This lecture covers key concepts in pattern recognition and classification, focusing on feature types, transformations, and dimensionality reduction methods such as PCA and ICA. It introduces Bayesian decision theory and methods for estimating class probabilities and parameters. Both parametric and non-parametric classification techniques are presented, including maximum likelihood, k-NN, and Parzen windows. A range of classifiers is discussed, such as SVMs, decision trees, perceptrons, and classifiers for sequences and nominal features. The lecture also addresses challenges like overfitting and explores learning principles, performance evaluation, and boosting.

Optimization Methods for Machine Learning and Engineering

Lecture, IES, KIT, 2020–2022

The term optimization refers to techniques for the identification of the best solution in a complex problem setting. Many applications from machine learning and engineering are based on solving an optimization problem. This lecture introduces the major theoretical and algorithmic tools for solving of convex optimization problems. Practical problems for machine learning, engineering and further application domains are used as illustration. The students apply their knowledge to practical optimization problems in tutorial exercises.