URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=2IIG0&collegejaar=2025&taal=enOmschrijving
Week 1: Introduction to data mining, machine learning and optimization; Instructions: Python for Data Mining and Machine Learning; Recap mathematical basicsWeek 2: Classification: Naive Bayes, K-Nearest Neighbors, Support vector machines and the Kernel Trick, Decision Trees and Random Forests; Evaluation of classifiers; Instructions: Python for Data Mining and Machine Learning; Recap mathematical basics
Week 3: Regression (linear and non-linear), regularization, bias-variance trade-off, Ridge regresseion, Lasso
Week 4: Neural Networks (Multi-layer Perceptrons and Convolutional Neural Networks), Backpropagation of Error, Stochastic Gradient Descent, training techniques
Week 5: Unsupervised Learning 1, PCA, SVD, Recommender systems, K-Means
Week 6: Unsupervised Learning 2, Gaussian Mixture models, Expectation Maximization, Non-convex clustering
Week 7: Evaluation, Guest lecture (prospectively Responsible Data Science), Overview Reinforcement Learning and Recurrent Neural Networks
Week 8: Preparation for Exam, example questions, Q&A
Doelstellingen
The main focus of this course is on the theoretical and mathematical foundations of Data Mining and Machine Learning. A secondary focus is on low-level practical aspects (e.g. vanilla implementations of various models and algorithms). After the course the students will be able to:* define Data Mining
* define Machine Learning
* define and explain the three main Machine Learning paradigms: supervised and unsupervised.
* perform basic work with data
* identify and argue which Machine Learning methods are the most suitable for a specific learning problem
* derive, implement, and evaluate some of the most widely used methods (listed in the course content) for a specific learning paradigm.
* derive, implement, and evaluate some of the most used deep learning models (listed in the course content) and their learning algorithms
* Apply Machine Learning models to Data Mining problems