README.md

Maschine_Learning_and_Energy

This project contains the notebooks from one of my machine learning modules at university.

Description

This Project contains Exercises from the Module Machine Learnig and Energy that includes:

• Basic EDA
• Clustering
  • K-Means
• Classification:
  • SVM
  • Nearest neigbours
  • Random Forests
  • CNN
  • Decision Trees
• Regression
  • SVR
  • KNN
  • Random Forests
  • Linear Regession
  • Ridge Regression
  • Logistic Regression
  • LASSO
• Dimentionallity reduction
  • K-Means
  • PCA
  • Feature Selection

Overview Exercise Content

• Ex0: Solving an electrical circuit

• Ex1: Optimization and gradient decent

• Ex2 power plant: Optimization using linesearch and gradient decent, KNN regressor, cross validation

• Ex3 load forcasting: Linear regression, polynomial features

• Ex4 Electrical Failure Analysis:

• Ex5 deep learning:

• Ex6 Image classification with CNNs: TensorFlow, Keras

• Ex7 Principal Component Analysis and Normal Distribution

• Ex8 Bayes' Theorem: k-Means clustering and EM for Gaussian mixtures

• Ex9 Probabilistic Graphical Models: Computing probabilities