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Julian Lotzer's Website
Here you will find materials for my exercise sessions in Stochastics and Machine Learning, Computer Science I, Computer Science II, and additional Mechanical Engineering content. You can sign up for my mailing list here:
Mailing listStochastics and Machine Learning
This section contains the weekly exercises and teaching materials for the second part of the lecture Stochastics and Machine Learning. If the downloads are not working, simply click here, log in, and reload the page.
PVK Skript
Feel free to use my PVK Skript as an additional ressource for your exam preparation. If you find any mistakes or have feedback, please let me know via E-Mail.
PDF DownloadSummary Recommendation 1
This summary is written by Emilio Besana and will updated on a regular basis.
PDF DownloadSummary Recommendation 2
This summary is written by Martin Mason and will updated on a regular basis.
PDF DownloadPolybox
My Polybox (Passwort: hier klicken) containing old exams, summaries and notes to many subjects:
Polybox
Week 9
linear regression, data imputation, one-hot encoding, bias and variance, introduction to project 1
- linear regression
- bias
- variance
Week 10
Statistical inference, maximum likelihood estimation (MLE), maximum a posteriori (MAP), Bayesian inference, introduction to project 2
- MLE
- MAP
- Bayesian Inference
Week 11
Ensemble Methods, Bagging and Boosting, Random Forest, Unsupervised Learning, PCA, K-Means Clustering
- Bagging
- Boosting
- PCA
- K-Means
Week 12
Neural Networks, Deep Learning, Convolutional Neural Networks (CNNs)
- Deep Learning
- CNN
Week 13
Natural Language Processing, C-BOW, Skip-Gram, Embeddings, Transformers, Attention is all you need
- NLP
- Transformers
- Attention
Week 14
Autoencoders, Denoising using Autoencoders, Generative Models, GANs, Reinforcement Learning, MDP's, Value Functions, Q-Learning
- Autoencoders
- RL
- Q-Learning