Machine Learning

This repository covers my participation in the courses

✔️ RWTH Computer Vision (2018)
✔️ RWTH Artificial Neural Networks (2019)
✔️ RWTH Machine Learning (2019)
✔️ RWTH Data Mining in the Domain of Technical Processes (2019)
✔️ Udacity Machine Learning Nanodegree (2020)
✔️ Coursera Reinforcement Learning: Fundamentals (2020)
✔️ Coursera Reinforcement Learning: Sample-based Learning Methods (2020)
✔️ Deep Q Learning (2021)

Overview

The courses cover the tiers of machine learning regularly recognized today:

Supervised Learning

Studying labeled data, these techniques can extend patterns to unlabeled data. Classification: Categorial Outcomes Regression: Numeric Outcomes Deep learning can be used within supervised machine learning to create techniques that are better at image recognition or identifying when a movie was created based on the video footage.

Unsupervised Learning

By learning patterns even when data do not have labels, these techniques can group items together that are likely to be similar.

Reinforcement Learning

By rewarding actions, these techniques focus on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

Biological Modelling

• 1: Hodgkin-Huxley
• 2: Integrate-And-Fire
• 3: Transmitter-activated ion channels
• 4: Synaptic Plasticity

Supervised Learning

• 1: Introduction
• 2: Linear Regression
• 3: Perceptron Algorithm
• 4: Decision Trees
• 5: Naive Bayes
• 6: Support Vector Machines
• 7: Ensemble Methods
• 8: Model Evaluation Metrics
• 9: Training and Tuning
• 10: Project: Income Prediction
• 11: Genetic Algorithms

Deep Learning

• 1: Introduction
• 2: Gradient Descent
• 3: Training Techniques
• 4: TensorFlow Usage
• 5: Project: Image Classification
• 6: Convolutional Neural Networks CNN
• 7: Recurrent Neural Networks RNN

Unsupervised Learning

• 1: K-Means Clustering
• 2: Hierarchical and Density Clustering
• 3: Gaussian Mixture Model Clustering
• 4: Dimensionality Reduction and PCA
• 5: Random Projection and ICA
• 6: Project: Customer Segment Identification
• 7: Self-Organizing Map SOM
• 8: Adaptive Resonance Theory ART
• 9: Pulse-coupled Neural Networks PCNN

Reinforcement Learning

• 1: Exploration-Exploitation Trade-Off
• 2: Markov Decision Processes
• 3: Value Functions & Bellman Equations
• 4: Dynamic Programming
• 5: Monte Carlo Methods
• 6: Temporal Difference Learning
• 7: SARSA & Q-Learning
• 8: Dyna Architecture
• 9: Neural Network Function Approximation
• 10: Deep Q-Learning DQN