Home

Welcome to the Machine Learning wiki!

Schedule

Motivation and Introduction

• Perceptron, e.g. Linear Classifier; Day 1️⃣ 02.02.2023
• Multi Layer Perceptron, Day 2️⃣ 09.02.2023
• Feed Forward, Activation Function and Back Propagation, Day 2️⃣ 09.02.2023
• Examples: XOR problem, doodle classifier, Day 3️⃣ 23.02.2023, Recap Day 4️⃣ 09.03.2023

Convolutional Neural Network

• Motivation, Convolution and Application to Images Day 5️⃣ 14.03.2023
• CNN Architecture, Day 6️⃣ 23.03.2023

Introduction into Tensorflow

• Tensors and operations, models and layer etc., Day 7️⃣ 30.03.2023, 8️⃣ 17.04.2023
• Example: doodle classifier with CNN, Day 9️⃣ 21.04.2023

Advanced Topics

• Types Learning Strategies
  • Supervised, Unsupervised and
  • Reinforced Learning
    • Traditional Q-value approach, Day 🔟 27.04.2023
    • Deep Q-Networks, Day 1️⃣ 1️⃣ , 04.05.2023
• Types of Networks, Architectures and Layers
  • Autoencoder, Day 1️⃣ 2️⃣ , 25.05.2023
  • RNN, GAN etc.
• Transfer Learning
• Genetic Algorithm, Day 1️⃣ 3️⃣, 01.06.2023
  • General Idea and application to ML
  • Example: Flappy Bird

Digital Signal Processing

• Common Understanding
• FFT, Windowing, Spectograms, Day 1️⃣ 4️⃣ , 08.06.2023
• Mel Filter, 1️⃣ 5️⃣ , 15.06.2023
• Application of ML on Audio Data

Audio Examples, Demos

• Typical workflow
• Data taking and labeling
• Data augmentation
• Live Wake Up Word detection, 1️⃣ 6️⃣ 22.06.2023
• Noise reduction