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Machine Learning Lecture

Intro and Overview Machine Learning Lecture

Introduction

Basic Concepts of Data Mining and Machine Learning

Conventional ML

K - Nearest Neighbour Classification / Regression
Bayes- and Naive Bayes Classifier
Linear Regression
Example Linear Regression
Linear Classification
Support Vector Machines (SVM)
Gaussian Process
Gaussian Process: Implementation in Python

Neural Networks

Neural Networks Introduction
Recurrent Neural Networks
Convolutional Neural Networks
Animations of Convolution and Deconvolution
Implementing Neural Networks with Keras
Applying Pretrained Deep Neural Networks for Image Classification
Apply Pretrained Neural Networks on new Task

Autoencoder

Variational Autoencoder (VAE) to generate handwritten digits

GAN

Generative Adversarial Nets (GAN)
DCGAN Keras Implementation

Diffusion Models

Difussion Models

Reinforcement Learning

Reinforcement Learning
Deep Reinforcement Learning
Example Q-Learning

Text

Representations for Words and Texts
Text classification with CNNs and LSTMs

Graph Neural Networks

Graph Neural Networks (GNN)

Transformer

Sequence-To-Sequence, Attention, Transformer
Intent Classification with BERT

References

References

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By Prof. Dr. Johannes Maucher
© Copyright 2022.