Advanced Machine Learning

This specialization recaps quickly the machine learning and especially deep learning principles.

The student dives into the following topics

• Advanced Feature Engineering Methods
  • Anomaly detection
  • Autoencoders
• Generative Models
  • Variational Autoencoders
  • Generative Adversarial Networks
• Explainable Machine Learning
• Reinforcement learning

by filling their knowledge gaps between theory and practice while applying the methods in python solving natural language understanding and special computer vision real-world problems

for being able to apply modern machine learning methods in enterprises and research and understand the caveats of real-world data and settings.

• ML and DL principles (recap)
• Advanced Feature Engineering Methods
  • Anomaly detection
    • Standardization,Box Plots,Correlation,DB-Scan Clustering,Isolation Forest,Robust Random Cut Forest
  • Autoencoders
    • feature selection and feature extraction
    • Latent variables and spaces
    • Image denoising
    • Missing value imputation / image impainting
    • Domain adaptation
• Generative Models
  • Variational Autoencoders
  • Generative Adversarial Networks
• Explainable Machine Learning
  • XAI methods and definitions
  • Partial Dependence Plots
  • Individual Conditional Expectation
  • Centered Individual Conditional Expectation
  • Derivative Individual Conditional Expectation
  • Shapley Values
  • Local Interpretable Model-agnostic Explanations (LIME)
• Reinforcement learning
  • Definitions
  • Reinforcement control loop
  • Markov Decision process
  • Transition Probabilities
  • Discounted and Expected Return
  • Policies And Value Functions
  • The exploration-exploitation dilemma
  • Q-Learning
  • Deep Reinforcement Learning

• Lecture
• Exercises and software development (notebooks)
• Accompanying project work by analyzing data sets

Learning Material Provided by Lecturer

• List of selected literature and web resources
• Lecture slides
• Video tutorials
• Exercises and code tutorials
• Example code and notebooks on github and Colab
• Data sets and models

• Ian Goodfellow, Yoshua Bengio und Aaron Courville: Deep Learning (Adaptive Computation and Machine Learning), MIT Press, Cambridge (USA), 2016. ISBN 978-0262035613 .
• Neural Networks and Deep Learning by Michael Nielsen, ONline Book, http://neuralnetworksanddeeplearning.com/
• Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. "The elements of statistical learning". www.web.stanford.edu/~hastie/ElemStatLearn/ (2009)
• Doshi-Velez, Finale, and Been Kim. "Towards a rigorous science of interpretable machine learning," no. Ml: 1–13. http://arxiv.org/abs/1702.08608 (2017)