“Deep Learning and Artificial Intelligence” Tutorial

Deep Learning and Artificial Intelligence Tutorial @ LMU WS 2018/19

University of Munich, Winter Semester 2018/19, Course Homepage

• Responsible Professor: Prof. Dr. Matthias Schubert
• Lecturers: Dr. Florian Büttner, Dr. Markus Geipel, Pankaj Gupta, Dr. Denis Krompass, Prof. Dr. Matthias Schubert, Dr. Sigurd Spieckermann, Prof. Dr. Volker Tresp
• Assistants: Sebastian Schmoll, Sabrina Friedl
• Tutor: Changkun Ou

Time: Monday, 2pm-4pm or 4pm-6pm.

Tutorial sessions

• 2018.10.22 Week 1: Python Introduction
  • Jupyter Notebook: Python introduction
• 2018.10.29 Week 2: Derivative, Jacobian Matrix, Mean Square Error
  • Exercise solution: Manuscript
• 2018.11.05 Week 3: Computational Graph, Computational Gradient Graph, Backpropagation (BP), Gradient Vanishing & Exploding Problem
  • Exercise solution: Manuscript
  • Jupyter Notebook: Computational graph numpy implementation
• 2018.11.12 Week 4: Convolution, Cross-correlation, ConvLayer and ConvNet
  • Exercise solution: Manuscript
  • Jupyter Notebook: Cross-correlation numpy implementation
  • Jupyter Notebook: Tensorflow Introduction, CNN, Inception
• 2018.11.19 Week 5: Backpropagation through Time (BPTT), Gradient Vanishing/Exploading in RNN, LSTMs, CIFAR10
  • Exercise solution: Manuscript
• 2018.11.26 Week 6: Statistic Uncertainty, Evidence Lower Bound, Metropolis-Hastings Algorithm, LSTM
  • Exercise solution: Manuscript
  • Jupyter Notebook: Metropolis-Hastings Algorithm
  • Jupyter Notebook: LSTM tensorflow implementation
• 2018.12.03 Week 7: Local and distributed representation, Autoencoders, Restricted Boltzmann Machines
  • Exercise solution: Manuscript
  • Jupyter Notebook: Autoencoder
  • Jupyter Notebook: RBM
• 2018.12.10 Week 8: Tooling, PyTorch Introduction
  • Exercise solution: text
  • Jupyter Notebook: PyTorch Introduction
• 2018.12.17 Week 9: Variational Autoencoder, GANs
  • Exercise solution: Manuscript
  • Jupyter Notebook: Variational Auto-Encoder
• 2019.01.07 Week 10: Markov Reward Process, Markov Decision Process and Policy Iteration
  • Exercise solution: Manuscript
  • Jupyter Notebook: Markov Decision Process
• 2019.01.14 Week 11: Model-free Reinforcement Learning, Temporal Difference Learning, Q-Learning and SARSA
  • Exercise solution: Manuscript
  • Jupyter Notebook: Model-free RL: Q-Learning and SARSA
• 2019.01.21 Week 12: Value Function Approximation, Baird’s Counterexample and Montain Car benchmark
  • Exercise solution: Manuscript
  • Exercise solution: Baird’s counterexample
  • Exercise solution: Q-learning in Montain Car
• 2019.01.28 Week 13: Policy Gradients and Actor Critic Learning
  • Exercise solution: Manuscript
  • Exercise solution: Policy gradient with baseline
• 2019.02.04 Week 14: Knowledge Graphs in AI
  • No tutorial

References

• LMU “Machine Learning” Tutorial Materials

License

MIT & CC-BY 4.0 Copyright © 2018-2019 Ou Changkun