• Machine Learning orientation
  • 1 Introduction
  • I Machine learning: Shall we?
  • 2 What is machine learning?
    • 2.1 What is intelligence?
      • 2.1.1 Definition of artificial intelligence sub domains
    • 2.2 Is AI smarter than humans?
      • 2.2.1 Thinking, fast and slow (Kahneman 2011)
    • 2.3 Comparisons between AI and humans
      • 2.3.1 Breast cancer detection
      • 2.3.2 Working together: Lung cancer detection
      • 2.3.3 ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
      • 2.3.4 AlphaGo Zero
    • 2.4 ML models with bias
    • 2.5 Attacks on ML models
      • 2.5.1 Adding noise to image leads to misclassification
      • 2.5.2 But what about attacks on human perception?
      • 2.5.3 Model Hacking ADAS to Pave Safer Roads for Autonomous Vehicles
  • 3 Outlook
    • 3.1 Development of life
      • 3.1.1 When will superhuman AI come, and will it be good?
      • 3.1.2 AI aftermath scenario
    • 3.2 Data religion: Dataism
  • 4 Discussion points
    • 4.1 Researcher stops his work due to ethical concerns
    • 4.2 Career: Oxford seeks AI ethics professor
    • 4.3 Shaping Europe’s digital future: Commission presents strategies for data and Artificial Intelligence
  • II Machine learning fundamentals
  • 5 Machine learning fundamentals
  • 6 ML project process
    • 6.1 Identify if ML is suited to fulfill need
    • 6.2 Gather data
      • 6.2.1 What kind of data can be used?
      • 6.2.2 How much data is necessary?
      • 6.2.3 Which data is useful?
    • 6.3 Exploratory and quantitative data analysis
      • 6.3.1 Example for exploratory and quantitative data analysis
      • 6.3.2 Visualizations for Categorical Data: Exploring the OkCupid Data
    • 6.4 Feature engineering
      • 6.4.1 Encoding Categorical Predictors
      • 6.4.2 Engineering numeric features
      • 6.4.3 Feature importance
    • 6.5 Model fit
    • 6.6 Model tuning
      • 6.6.1 Metrics
  • 7 Machine learning types
    • 7.1 Supervised learning
      • 7.1.1 Self supervised learning
    • 7.2 Unsupervised learning
      • 7.2.1 Discovering clusters
      • 7.2.2 Discovering latent factors
    • 7.3 Reinforcement learning
      • 7.3.1 Elements of reinforcement learning
      • 7.3.2 RL algorithms
      • 7.3.3 Example self driving car MIT
  • 8 ML application examples
    • 8.1 AI translation by DeepL
    • 8.2 Artificial intelligence detects myocardial infarctions in the ECG more reliably than cardiologistsn
    • 8.3 A Deep Learning Approach to Antibiotic Discovery
    • 8.4 BBC: Artificial intelligence-created medicine to be used on humans for first time
    • 8.5 Outbreak risk software
    • 8.6 Neuronale Netze ermöglichen autonome Steuerung von Kathetern
    • 8.7 Fundamental limits from chaos on instability time predictions in compact planetary systems
      • 8.7.1 Comparison between SPOCK and previous models
    • 8.8 Machine Learning Algorithms and Global Optimization Methods for SPICE Model Parameter Extraction
    • 8.9 Master Autonomous Driving
    • 8.10 University Suttgart: Indoor-Ortung mit Mobilfunk
  • 9 ML algorithms
    • 9.1 Linear regression
      • 9.1.1 Example for linear regression
    • 9.2 Logistic regression
      • 9.2.1 Python example logistic regression
    • 9.3 Tree based methods
      • 9.3.1 Splitting metrics
      • 9.3.2 Ensembles
      • 9.3.3 Random forest
      • 9.3.4 Boosted trees
    • 9.4 Support Vector Machine (SVM) TBD
      • 9.4.1 Kernels
      • 9.4.2 Python example for SVM
    • 9.5 Neural networks
      • 9.5.1 Convolutional Neural Network (CNN) TBD
      • 9.5.2 RNN TBD
      • 9.5.3 GANs
    • 9.6 A Gentle Introduction to CycleGAN for Image Translation
      • 9.6.1 Examples for GANs
    • 9.7 Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
    • 9.8 Transformers TBD
      • 9.8.1 Example transformer for time series forecasting
  • 10 Food for the algorithms: Data
    • 10.1 NLP
    • 10.2 Dataset search from Google
  • III Explainable ML
  • 11 Explainable ML tbd
    • 11.1 Method: Layer-Wise Relevance Propagation
    • 11.2 Method: SpRay
    • 11.3 Method: Lime tbd
    • 11.4 alibi tbd
    • 11.5 tf-explain tbd
    • 11.6 keras-salient-object-visualization
      • 11.6.1 Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
      • 11.6.2 VisualBackProp: efficient visualization of CNNs
  • IV ML online resources
  • 12 ML online courses
    • 12.1 Coursera
    • 12.2 Udemy
    • 12.3 DataCamp
    • 12.4 Udacity
      • 12.4.1 Example for self-driving car course project
    • 12.5 FastAI
    • 12.6 Kaggle Courses
    • 12.7 Full Stack Deep Learning
  • 13 ML online resources
    • 13.1 In-depth introduction to machine learning in 15 hours of expert videos
      • 13.1.1 An Introduction to Statistical Learning
    • 13.2 The learning machine
    • 13.3 DeepAI: The front page of A.I.
    • 13.4 TensorFlow tutorials
      • 13.4.1 MIT 6.S191 Introduction to Deep Learning
    • 13.5 Embedding Projector
    • 13.6 Tensorboard playground
    • 13.7 Empowering companies to jumpstart AI and generate real-world value
  • 14 ML online books
    • 14.1 Neural Networks and Deep Learning
    • 14.2 Deep Learning
  • V Examples from Kaggle
  • 15 Examples in Kaggle
  • 16 Melbourne University AES/MathWorks/NIH Seizure Prediction
    • 16.1 Winning solution (1st)
      • 16.1.1 Alex / Gilberto models
      • 16.1.2 Feng models
      • 16.1.3 Andriy models
      • 16.1.4 Code on GitHub
    • 16.2 Solution(4th place)
      • 16.2.1 Pre-processing
      • 16.2.2 Features
      • 16.2.3 Model
      • 16.2.4 GitHub code
  • 17 Bosch Production Line Performance
    • 17.1 1st place solution
      • 17.1.1 Data exploration
      • 17.1.2 Hand crafted features
      • 17.1.3 Hardware
    • 17.2 3rd place solution TBD
    • 17.3 8th place solution with GitHub
      • 17.3.1 Overall architecture
      • 17.3.2 Input data sets
      • 17.3.3 Ensembling
      • 17.3.4 Features
      • 17.3.5 Validation method
      • 17.3.6 Software
      • 17.3.7 Code on GitHub
  • 18 Corporación Favorita Grocery Sales Forecasting
    • 18.1 1st place solution
    • 18.2 4th-Place Solution Overview
    • 18.3 5th Place Solution
  • 19 Severstal: Steel Defect Detection
  • 20 Lyft 3D Object Detection for Autonomous Vehicles
    • 20.1 3rd place solution
  • 21 APTOS 2019 Blindness Detection
    • 21.1 1st place solution summary
  • 22 Predicting Molecular Properties
    • 22.1 #1 Solution - hybrid
      • 22.1.1 Overall architecture
      • 22.1.2 Ensembling
      • 22.1.3 Hardware
      • 22.1.4 Software
      • 22.1.5 Code on GitHub
    • 22.2 #2 solution 🤖 Quantum Uncertainty 🤖
      • 22.2.1 Overall architecture
      • 22.2.2 Input features and embeddings
      • 22.2.3 Data augmentation
      • 22.2.4 Ensembling
      • 22.2.5 Hardware
      • 22.2.6 Software
      • 22.2.7 Code on GitHub
  • VI Real world example
  • 23 Real world example
    • 23.1 Subject of the project
      • Depending from where you were looking:
      • Looking from the perspective of machine learning expert
    • 23.2 Project phases
      • The main project phases are:
      • After data gathering iteration is trump
      • 23.2.1 Feature engineering
    • 23.3 Algorithm selection
      • 23.3.1 Logistic regression
      • 23.3.2 Tree based
      • 23.3.3 Support Vector Machine (SVM) TBD
    • 23.4 Performance measurement
      • 23.4.1 Sensitivity and specificity
      • 23.4.2 Receiver operating characteristic (ROC)
    • 23.5 Confusion matrix and receiver operating characterstic (ROC) for pulse
      • 23.5.1 Receiver operating characterstic (ROC) and probability density plots
    • 23.6 Create augmented labeled data
      • 23.6.1 Features of time signals
    • 23.7 Features generated
      • 23.7.1 Analysis of generated features
      • 23.7.2 Dynamic time warp (DTW) for signal
    • 23.8 Algorithm
    • 23.9 Confusion matrix results logistic regression for measured data
      • 23.9.1 ROC results for measured data
    • 23.10 Several algorithms results for SNR = 18dB
      • 23.10.1 ROC results for SNR 18dB
    • 23.11 Calculation of return of invest (ROI)
      • 23.11.1 Calculation of ML project invest
      • 23.11.2 Calculation of ML profit
      • 23.11.3 Resulting ROI
    • 23.12 Compare models for SNR = 18dB
    • 23.13 Optimize ML hyper parameter
  • VII Cloud-based machine learning
  • 24 Cloud-based machine learning
  • VIII Kaggle Survey
  • 25 Kaggle survey introduction
    • 25.1 Kaggle survey details
    • 25.2 Purpose
    • 25.3 Navigation and handling
  • 26 Results
    • 26.1 Survey participants education level
    • 26.2 Who uses which algorithm
    • 26.3 Machine learning experience and algorithms
    • 26.4 Experience and new algorithms
    • 26.5 Role of participants
    • 26.6 Company size
    • 26.7 Company incorporation of machine learning
    • 26.8 Favourite media sources on data science topics
    • 26.9 Favourite online course platform
    • 26.10 Favourite data analyzing tool
    • 26.11 Experience in data analysis coding
    • 26.12 Favourite integrated development environments (IDE’s)
    • 26.13 Favourite hosted notebook products
    • 26.14 Favourite programming languages
    • 26.15 Recommended entry programming language
    • 26.16 Favourite data visualization libraries or tools
    • 26.17 Favourite specialized hardware
    • 26.18 Favourite machine learning frameworks
    • 26.19 Favourite cloud computing platforms
    • 26.20 Favourite big data / analytics products
    • 26.21 Favourite automated machine learning tools (or partial AutoML tools)
  • References
  • Published with bookdown

Machine learning orientation

8.6 Neuronale Netze ermöglichen autonome Steuerung von Kathetern

https://www.fraunhofer.de/de/presse/presseinformationen/2019/november/neuronale-netze-ermoeglichen-autonome-steuerung-von-kathetern.html

Künstliche Intelligenz für die Medizintechnik

https://idw-online.de/de/attachmentdata80143