- Einführung in Data Science und maschinelles Lernen
- Bedingungen für ein Leistungszertifikat oder ECTS
- Vorbereitung
- Woche 1 - Einführung in Data Science
- Woche 2 - Import und Visualisierung von Daten
- Woche 3 - Versionierung mit git (Teil 1) und Datenaufbereitung
- Woche 4 - Versionierung mit git (Teil 2) und aktuelle Entwicklungen im Bereich ML
- Woche 5 - Einführung in das maschinelle Lernen
- Woche 6 - Overfitting und Regularisierung
- Woche 7 - Neuronale Netze
- Woche 8 - Fehlende Werte
- Woche 9 - Zeitreihenanalysen
- Woche 10 - Projektpräsentationen
- Machine Learning with TensorFlow
- Requirements for a Certificate of Achievement or ECTS
- Preparation
- Week 1 - General Introduction
- Week 2 - Introduction to TensorFlow,Part I
- Week 3 - Introduction to TensorFlow,Part II
- Week 4 - Convolutional Neural Networks, Part I
- Week 5 - Convolutional Neural Networks, Part II
- Week 6 - Natural Language Processing, Part I
- Week 7 - Natural Language Processing, Part II
- Week 8 - Project Work
- Week 9 - Sequences, Time Series and Prediction, Part I
- Week 10 - Sequences, Time Series and Prediction, Part II
- Week 11 & 12 - Presentation of the Final Projects
- Intermediate Machine Learning
- Hello and welcome😊
- Prequisites
- Week 1 - Course Introduction
- Week 2 - Recap ML Basics, Intro to PyTorch
- Week 3 - Intro Kaggle competition - EDA and baseline models with PyTorch
- Week 4 - Convolutional Neural Networks
- Week 5 - Recurrent Neural Networks
- Week 6 - CNN and RNN Applications
- Week 7 - Transformers & Hugging Face
- Week 8-10 - Kaggle competiton sessions
- Week 11 - Final Presentations
- Advanced Time Series Prediction
- Requirements for a Certificate of Achievement or ECTS
- Projects & Frameworks
- Lecture material + YouTube
- References / Books
- Week 1 - Intro + Organisation
- Week 2 - SARIMA(X) + GARCH-Models
- Week 3 - Labour Day
- Week 4 - State-Space models // Filtering
- Week 5 - Dependence concepts: Copula // Gaussian Processes // RMT
- Week 6 - Extremes // Anomalies // Signatures
- Week 7 - Tree models: XGBoost // LightGBM // CatBoost
- Week 8 - (Deep) recurrent architectures for time series data
- Week 9 - Transformers + TemporalFusionTransformers
- Week 10 - NBEATS(x) + NHITS
- Week 11 - LLM for time series problems
- Week 12 - Final Presentations
- Week 13 - Final Presentations (Back-Up)
- Python: From Beginner to Practitioner
- Fine-Tuning and Deployment of Large Language Models
- Requirements for a Certificate of Achievement or ECTS
- Preparation
- Week 1 - General Introduction
- Week 2 - Project Definition and Introduction to Fine-Tuning
- Week 3 - Fine-Tuning Characteristics
- Week 4 - Model Evaluation
- Week 8 - Tokenization for Instruction Tuning
- Week 9 - Efficient Inference
- Week 10 - Project Presentations
- Archive
- Deep Learning from Scratch
- Requirements for a Certificate of Achievement or ECTS
- Preparation
- Week 1 - General Introduction
- Week 2 - Introduction to Deep Learning and Neural Network Basics
- Week 3 - Shallow Neural Networks
- Week 4 - Deep Neural Networks
- Week 5 - Practical Aspects of Deep Learning
- Week 6 - Optimization Algorithms
- Week 7 - Hyperparameter Tuning
- Week 8 - Machine Learning Strategy 1 & 2
- Week 9 - Neural Networks Architecture | Project Checkpoint
- Week 10 - Bonus: most voted topic
- Week 11 - Presentation of Final Projects, Part I
- Week 12 - Presentation of Final Projects, Part II
- Deep Learning for Computer Vision
- Requirements for a Certificate of Achievement or ECTS
- Preparation
- Week 1 - General Introduction
- Week 2 - Foundations of Convolutional Neural Networks
- Week 3 - Convolution Model Application
- Week 4 - Residual Networks
- Week 5 - Transfer Learning
- Week 6 - Detection Algorithms
- Week 7 - Project Checkpoint | Image Segmentation
- Week 8 - Face Recognition
- Week 9 - Art Generation with Neural Style Transfer
- Week 10 - CNN Bonus
- Week 11 - Final Presentation of the Projects
- Application of Transformer Models
- Requirements for a Certificate of Achievement or ECTS
- Week 1 - General Introduction
- Week 2 - Self-Attention and Prompt Design
- Week 3 - Introduction to Transformer Models
- Week 4 - Fine-Tuning Pretrained Models
- Week 5 - The Datasets Library
- Week 6 - The Tokenizers Library
- Week 7 - Main NLP Tasks
- Week 8 - Presentation of the Final Projects
- Generative Adversarial Networks
- Requirements for a Certificate of Achievement or ECTS
- Preparation
- Motivation - Things you can do with NLP
- Week 1 - General Introduction to the course
- Week 2 - Sentiment Analysis with Logistic Regression
- Week 3 - Sentiment Analysis with Naïve Bayes
- Week 4 - Vector Space Models
- Week 5 - Machine Translation and Document Search
- Week 6 - Autocorrect
- Week 7 - Part of Speech Tagging and Hidden Markov Models
- Week 8 - Autocomplete and Language Models
- Week 9 - Word embeddings with neural networks
- Week 10 - Final Projects
- Lehren und Lernen mit KI
- Woche 1 - Einführung
- Woche 2 - Anwendungsbeispiele #twlz
- Woche 3 - KI-Tools für den Bildungsbereich
- Woche 4 - Nicht-technische Einführung in die KI
- Woche 5 - Kreatives Schreiben
- Woche 6 - Automatische Klassifizierung von Textantworten
- Woche 7 - IQSH Handreichung zu CHatGPT
- Woche 8 - Veränderungen in benötigten Kompetenzen
- Woche 9 - Präsentation Abschlussprojekte
- Reinforcement Learning
- Machine Learning Operations (MLOps)
- 19-04-2023 - General Introduction
- 26-04-2023 ML Lifecycle Overview and Model Selection
- 03-05-2023 Data Definition and Collection
- 10-05-2023 From Feature Engineering to Data Storage
- 17-05-2023 Advanced Data Processing & Intro into Model Serving
- 24-05-2023 Model Infrastructure & Delivery
- 31-05-2023 Model Monitoring
- 07-06-2023 Project Presentations
- Mathematik für maschinelles Lernen
- TensorFlow Course: Week 10 - Special Issues Considering Your Final Projects
- Deep Dive into LLMs
- Week 1 - Introduction
- Week 2 - Tokens & Embeddings revisted
- Week 3 - Introduction to Transformers
- Week 4 - Prompt Engineering
- Week 5 - RAG and Agents
- Week 6 - Model Evaluation
- Week 7 - Fine-Tuning I
- Week 8 - Fine-Tuning II and Model Inference
- Week 9 - Advisory Session
- Week 10 - Project Presentations
- Intermediate Machine Learning (Legacy SS2023)
- Hello and welcome😊
- Prequisites
- Week 1 - Course Introduction
- Week 2 - Recap ML Basics, Intro to PyTorch
- Week 3 - Intro Kaggle competition - EDA and baseline models with PyTorch
- Week 4 - Convolutional Neural Networks
- Week 5 - Recurrent Neural Networks
- Week 6 - CNN and RNN Applications
- Week 7 - Transformers Part 1
- Week 8 - Transformers Part 2
- Week 9 - Vision Transformers
- Week 10-12 - Projects sessions
- Week 13 - Project Presentations
- Week 14+
- Practical Engineering with LLMs
- Week 1- General Introduction
- Week 2 - Prompt Engineering
- Week 3 - Introduction to LangChain
- Week 4 - Introduction to Retrieval Augmented Generation
- Week 5 - Advanced Retrieval Augmented Generation
- Week 6 - Building User Interfaces with Gradio
- Week 7 - Evaluation of LLM outputs and structured outputs
- Week 8 - Open-Source LLMs
- Week 9 - Project Presentations
- Python: From Beginner to Practictioner (Legacy WS2023)
- Machine Learning für die Medizin
- Time Series Prediction
- Requirements for a Certificate of Achievement or ECTS
- Projects & Frameworks
- Preparation / YouTube
- References / Books
- Week 1 - Intro + Organisation
- Week 2 - Forecasting basics with trends: AR + MA-models
- Week 3 - Covering seasonality: From ARMA to SARIMA-models
- Week 4 - Towards multidimensional settings: SARIMAX + VAR-models
- Week 5 - Non-Stationary model classes: GARCH + DCC-GARCH
- Week 6 - Copula Methods
- Week 7 - Milestone Meeting + Spectral Analysis of Time Series + Kalman-Filtering
- Week 8 - Supervised Learning I: Trees + Random Forests + Boosting
- Week 9 - Supervised Learning II: XGBoost + LightGBM + CatBoost
- Week 10 - Neural Networks for Sequences: RNNs + GRUs + LSTMs + LMUs
- Week 11 - Prophet(Facebook) + DeepAR(Amazon) + GPVAR
- Week 12 - Transformers + TFTs
- Week 13 - NBEATS(s) + NHITS(x)
- Week 14 - Final Presentation
- Python: From Beginner to Practitioner (Legacy 2024S)
- Deep Learning from Scratch