02450 Introduction to Machine Learning and Data Modeling
|Introduktion til machine learning og datamodellering|
|Taught under open university|
|E4A and F4A|
Scope and form:
The activities alternate between lectures, problem classes and hands-on Matlab, R or Python exercises (the student can freely choose between these programming languages). Exercises are carried out in teams of 2-3 students.
Duration of Course:
Date of examination:
Type of assessment:
General course objectives:
To provide the participants knowledge of
* fundamental and widely applied methods for data modeling and machine learning,
* a framework for data modeling,
* Matlab, R or Python as a tool for data analysis (the participant can freely choose between these programming languages).
The course enables the participants to apply machine learning for modeling of real world data.
|A student who has met the objectives of the course will be able to:|
- Describe the major steps involved in data modeling from preparing the data, modeling the data to evaluating and disseminating the results.
- Discuss key machine learning concepts such as feature extraction, cross-validation, generalization and over-fitting, prediction and curse of dimensionality.
- Sketch how the data modeling methods work and describe their assumptions and limitations.
- Match practical problems to standard data modeling problems such as regression, classification, density estimation, clustering and association mining.
- Apply the data modeling framework to a broad range of application domains in medical engineering, bio-informatics, chemistry, electrical engineering and computer science.
- Compute the results of the data modeling framework by use of Matlab, R or Python.
- Use visualization techniques and statistics to evaluate model performance, identify patterns and data issues.
- Combine and modify data modeling tools in order to analyze a data set of their own and disseminate the results of the analysis.
Structured data modelling. Data preprocessing. Feature extraction and dimensionality reduction including principal component analysis. Similarity measures and summary statistics. Visualization and interpretation of models. Overfitting and generalization. Classification (decision trees, nearest neighbor, naive Bayes, neural networks, and ensemble methods.) Linear regression. Clustering (k-means, hierarchical clustering, and mixture models.) Association rules. Density estimation and outlier detection. Applications in a broad range of engineering sciences.
Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, "Introduction to Data Mining."
The course is a basic machine learning course relevant for all technical diploma, bachelor, and master programs. The course is designed as a stand-alone course and provides a complete set of basic competence and hands-on experience.
Green challenge participation:
Please contact the teacher for information on whether this course gives the student the opportunity to prepare a project that may participate in DTU´s Study Conference on sustainability, climate technology, and the environment (GRØN DYST). More information
|, 321, 118, (+45) 4525 3900,
, 321, 116, (+45) 4525 5270,
, 321, 015, (+45) 4525 3923,
|02 Department of Informatics and Mathematical Modeling|
Registration Sign up:
|machine learning, data modeling framework, applications in engineering|
April 27, 2012|
See course in DTU Course base