Seminar: Basic Model in Data Analysis
InstructorProf. Dr. Martin Gebert
Seminar
Wednesday 2pm-4pm, online zoom
Description
We go through the basic models in data science:
- Linear regression, ridge regression, k nearest neighbors (kNN)
- Subset selection: Forward and backward stepwise selection, LASSO
- Principle component analysis (PCA)
- Classification I: Metrics (accuracy, precision,...), naive Bayes
- Classification II: Logistic regression
- Classification III: Support vector machine (SVM), embeddings; kernel trick
- Unsupervised learning: k means, spectral clustering, t-SNE
- Categorical and regression trees (CART), random forest, AdaBoost
- Neural nets, convolutional neural nets (CNN), recurrent neural nets (RNN)
- Tfidf vectorization, word2vec
- Time series analysis, wavelets
Prerequisites
Analysis I-II, Linear Algebra I-II, Numerics
Books
- Elements of Statistical Learning by Hastie, Tibshirani, and Friedman
- Deep Learning by Goodfellow, Bengio, and Courville