Supervised Machine Learning for Engineers
Master practical machine learning with Python and scikit-learn. Learn classification, regression, and model optimization for real engineering applications.
Master practical machine learning with Python and scikit-learn. Learn classification, regression, and model optimization for real engineering applications.
This comprehensive course teaches engineers the fundamentals of supervised machine learning using Python and scikit-learn. Learn to apply classification and regression techniques to real-world engineering problems. Through hands-on exercises, master essential concepts from basic algorithms to advanced topics like Support Vector Machines and Decision Trees. Explore model evaluation, optimization techniques, and practical implementation using Python. The course combines theoretical understanding with practical application, culminating in a final project building a complete machine learning pipeline for handwritten digit recognition.
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What you'll learn
Apply machine learning algorithms using Python and scikit-learn
Implement regression and classification techniques for real engineering problems
Evaluate and optimize machine learning models using various metrics
Understand and mitigate overfitting through regularization techniques
Develop complete machine learning pipelines from data preprocessing to model evaluation
Master practical applications of Support Vector Machines and Decision Trees
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 10 modules in this course
The course provides a comprehensive introduction to supervised machine learning for engineering applications. Starting with fundamental concepts, it covers both regression and classification techniques. Students learn about various algorithms including linear regression, K-nearest neighbors, Support Vector Machines, and Decision Trees. The curriculum emphasizes practical implementation using Python and scikit-learn, with focus on model evaluation, optimization, and handling real-world data challenges. Advanced topics include overfitting prevention, regularization techniques, and model evaluation metrics. The course concludes with a practical project on handwritten digit recognition, allowing students to apply their knowledge in a real-world scenario.
Introduction
Module 1
Regression
Module 2
Classification
Module 3
Training Models
Module 4
Cross Validation & Regularization
Module 6
Classifier Evaluation
Module 7
Support Vector Machines
Module 8
Decision Trees
Module 9
Final Project
Module 10
Fee Structure
Instructors
4 Courses
Expert in Machine Learning Education and Learning Curve Research
Tom Viering serves as an Assistant Professor in the Pattern Recognition and Bio-Informatics research group at TU Delft's Faculty of Electrical Engineering, Mathematics & Computer Science, where he coordinates and teaches in the university's AI minor program. After completing his MSc in Computer Science Media Knowledge Engineering at TU Delft, he earned his PhD focusing on explainability, active learning, and learning curves. His research interests span theoretical machine learning topics, including statistical learning theory, active learning, and domain adaptation. As an educator, he has developed several courses from scratch, including 'Introduction to Machine Learning' and 'Capstone Applied AI project,' and co-created MOOCs on Supervised Machine Learning. His innovative teaching approach incorporates interactive Python widgets to enhance student understanding, and he actively works on developing open education materials for machine learning. Beyond teaching, his research contributions include significant work on learning curve patterns, generalization bounds, and the relationship between data quantity and performance in machine learning systems. He has published extensively in prestigious venues, with his work on learning curves, active learning, and AI safety gaining notable recognition in the academic community. As coordinator of the AI minor program, which launched in 2021, he plays a crucial role in making AI education accessible to engineers across various disciplines at TU Delft.
3 Courses
Expert in Bayesian Statistics and Inverse Problems
Hanne Kekkonen currently serves as an Assistant Professor at the Delft Institute of Applied Mathematics, Faculty of Electrical Engineering, Mathematics and Computer Science at TU Delft. After earning her Ph.D. in applied mathematics from the University of Helsinki in 2016, she expanded her expertise through research positions at the University of Warwick and University of Cambridge before joining TU Delft in 2020. Her academic focus centers on statistical inverse problems, uncertainty quantification, and Bayesian non-parametric models. As an applied mathematician, she has made significant contributions to the field of Bayesian statistics and inverse problems, with particular emphasis on edge-preserving random tree Besov methods and efficient Bayesian calibration of mechanical properties. Her teaching portfolio includes involvement in AI Skills programs, where she contributes to courses on machine learning techniques and statistical analysis. Dr. Kekkonen is actively engaged in mathematics outreach and continues to advance research in integration of active learning and MCMC sampling for efficient Bayesian calibration.
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