Summary of Advanced Statistical Machine Learning (A)

Sukolilo | Manyar | Tjokroaminoto
Kampus Institut Teknologi
Sepuluh Nopember Surabaya
Phone: 031-5994251-54, 5947274, 5945472
Fax: 031-5923465, 5947845

SS235334 - Advanced Statistical Machine Learning (3 SKS)

Announcements

Announcements

Announcements

Announcements

Announcements

RANCANGAN PEMBELAJARAN SEMESTER

Week 1

Slide-1: Introduction to Statistical Machine Learning

Slide-2: Data Analytics

credit-earning program of BDC competition: session 1 (Bagus Sartono) - Teknik Pembelajaran Mesin

credit-earning program of BDC competition: session 2 (Rangga Pratama) - Clustering in Business

credit-earning program of BDC competition: session 3 (Dedy Dwi Prastyo) - Unsupervised Machine Learning

credit-earning program of BDC competition: session 4 (Setia Pramana) - Bioscience Machine Learning

credit-earning program of BDC competition: session 5 (Sri Astuti Thamrin) - Supervised Learning (part 1)

credit-earning program of BDC competition: session 6 (Siti Mariyah) - Pemanfaatan Statistical Machine learning pada official statistics

credit-earning program of BDC competition: session 7a (R Bagus Fajriya Hakim) - Supervised Learning (part 2)

credit-earning program of BDC competition: session 7b (R Bagus Fajriya Hakim) - SVM

credit-earning program of BDC competition: session 7c (R Bagus Fajriya Hakim) - ANN

credit-earning program of BDC competition: session 8 (Yunanto Cahyo Putranto) - Business Intelligence

Week 1

Book: Yu-Wei, David Chiu - Machine Learning with R Cookbook_ Explore over 110 recipes to analyze data and build predictive models with the simple and easy-to-use R code-Packt Publishing (2015)

Article: Prescriptive analytics - Literature review and research challenges

Article: 50 Years of Data Science

Week 2

Slide-3: Kernel

Kernel Density Estimator (KDE)

Nonparametric regression using Kernel estimator

Reproducing Kernel Hilbert Space (RKHS)

Week 3

slide-3: Supervised Learning: Logistic Regression

slide-4: Supervised learning: SVM for Classification

illustration: SVM with polynomial kernel visualization

Week 4

Support Vector Regression - a tutorial (1)

Support Vector Regression - a tutorial (2)

slide-5: Support Vector Regression (simulation) updated Sept-2021

Week 5 Model and Feature Selection/Engineering. Performance Evaluation.

slide-7: Model Selection, Feature Selection, and Performance Evaluation (update)

Mitigating the Multicollinearity Problem and Its Machine Learning Approach - A Review

VSURF - An R Package for Variable Selection Using Random Forests

glmnet - Regularization Paths for Generalized Linear Models via Coordinate Descent

Statistical predictions with glmnet

penalizedSVM - a R-package for feature selection SVM classification

PlosONE The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets

Handling Imbalanced Class

Corporate Financial Distress Prediction using Statistical Extreme Value-based Modeling and Machine Learning

Handing Imbalanced Dataset - GEVR approach

week 6 Decision tree-based approach

slide-6: Supervised learning: Decision Tree and Random Forest (updated)

Random Forest for Regresion

Week 7 Data with mixture type &amp; extension of Kernel

kernlab – An S4 Package for Kernel Methods in R

SVM - The Interface to libsvm in package e1071

kernel PCA - Application of kernel PCA and computational machine learning to exploration of metabolites strongly associated with diet

mixKernel - Unsupervised multiple kernel learning for heterogeneous data integration

PCAmixdata R Package - Multivariate Analysis of Mixed Data

PCAmix - Outlier detection using PCA mix based T2 control chart

Week 9 Introduction to NN

Introduction to neural network

Implementation of simple NN using Python

Introduction to NN

neural networks and statistical models

neural networks and statistical models (Powell & Duffy)

Week 10 Simple network

Simple network

Textbook

Material : linearity test

White test

TErasvirta test

Perceptron

And problem

Week 11

Gradient descent

Tutorial Neural Network dengan Tensorflow

Multilayer perceptron

Gradient descent

backpropagation. xlxs

Backpropagation algorithm

Week 12

Week 7 Data with mixture type & extension of Kernel