GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → pilsung-kang → machine-learning-with-R

pilsung-kang / machine-learning-with-R

Licence: other
Machine Learning with R @fastcampus

Programming Languages

r
7636 projects

machine-learning-with-R

Machine Learning with R @FASTCAMPUS

Schedule

Week 1: Mahine Learning Overview

  • Introduction to data science
  • Data science applications
  • Machine learning in data science
  • Process of machine learning projects

Week 2: Introduction to R

  • Data types in R
  • Text data handling
  • Conditions and loops in R
  • Functions in R
  • Graphs in R

Week 3: Association Rule Mining (ARM)

  • A Ariori algorithm
  • ARM application 1: Market basket analysis
  • ARM application 2: Finding visiting patterns in exhibitions
  • ARM application 3: Recommendation of education programs
  • ARM application 4: Predictive maintanence marine structure
  • R Exercise

Week 4: Multiple Linear Regression (MLR)

  • Multiple linear regression: ordinary least squares (OLS)
  • Evaluating the performance of regression algorithms
  • Supervised variable selection
  • MLR application: Forecasting box office with SNS data
  • R Exercise

Week 5: k-Nearest Neigbhor Learning (k-NN)

  • k-NN classification
  • k-NN regression
  • Evaluating the performance of classification algorithms
  • k-NN application: spam filtering (classification) & collaborative filtering-based recommendation (regression)
  • R Exercise

Week 6: Decision Tree

  • Classification and Regression Tree (CART)
  • Recursive partitioning & Pruning
  • CART application: Late payment prediction model
  • R Exercise

Week 7: Naive Bayes and Logistic Regression

  • Naive Bayesian classifier
  • Logistic regression
  • Logistic regression application: customer response modeling in marketing
  • R Exercise

Week 8: Linear Discriminant Analysis and Artificial Neural Network

  • Linear discriminant analysis (LDA)
  • Artificial neural network (ANN)
  • Introduction to deep learning
  • Structure of convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN)
  • ANN application: Virtual metrology in semiconductor manufacturing
  • R Exercise

Week 9: Clustering

  • Goals and issues in clustering
  • K-Means clustering
  • Hierarchical clustering
  • Self organlizing map
  • R Exercise

Week 10: Ensemble

  • Background, motivation, and goals
  • Bagging
  • Boosting: Adaboost, Gradient boosting
  • Random forests
  • R Exercise
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].