machine-learning-with-R

Machine Learning with R @FASTCAMPUS

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