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SS234526 - Statistical Machine Learning
Announcements
Rencana Pembelajaran Semester (RPS) / Semester Learning Plan
Announcements
Week #1 - Introduction to machine learning
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 #1 - Introduction to machine learning (continued)
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 #2 - Clustering
slide of the MVA book (Haerdle and Simar) - Clustering
A Comprehensive Survey of Clustering Algorithms
MVAclusfood.R
food.dat
Unsupervised method - Clustering
W02-Fuzzy c-means clustering
W03-DBSCAN
Week#2: Lecture and Laboratory Exercise
Laboratory exercise: September 1, 2025, start at 10:00 WIB
Week #3 - more on Clustering
CC GENERAL.csv
Week #3 - Additional material: Clustering in Time Series
Short review of Clustering
Clustering in Time Series
Analisis Cluster untuk Data Time Series
reference 1 - Time-Series Clustering in R Using the dtwclust Package (the R Journal)
reference 2 - Comparing time series clustering algorithms in R using dtwclust package
reference 3 - Computing and Visualizing Dynamic Time Warping Alignments in R - The dtw Package (Journal of Stat Soft)
reference 4 - TSclust - An R package for time series forecasting (Journal of Stat Soft)
A Comprehensive Survey of Clustering Algorithms
Mutual Information-Based Variable Selection on Latent Class Cluster Analysis
Week #4 - Decision Tree
Tree-based Models
Decision Tree
Week #5 - Random Forest
Random Forest for Regresion
slide-6: Supervised learning: Decision Tree and Random Forest (updated)
Week #6 Support Vector Machine
slide-3: Supervised Learning: Logistic Regression
slide-4: Supervised learning: SVM for Classification
illustration: SVM with polynomial kernel visualization
Week #7 - Support Vector Regression
Support Vector Regression - a tutorial (1)
Support Vector Regression - a tutorial (2)
slide-5: Support Vector Regression (simulation) updated Sept-2021
Week #9 - Time Series with NN
Material MLP in Time Series
White test
TErasvirta test
Material : linearity test
Week #10 - Neural networks
Introduction to neural network
Simple network
Perceptron
Multilayer perceptron
And problem
neural networks and statistical models (Powell & Duffy)
NN in SPSS
NN in R
Recurrent Neural Networks
Tugas Neural Network
Textbook
Implementation of simple NN using Python
Convolutional Neural Network
Tutorial Neural Network dengan Tensorflow
Week 10 Neural Network
Materi Week 10
Week #11 - Backpropagation Neural Net
Gradient descent
Backpropagation algorithm
backpropagation. xlxs
Materi Week 11
Week #12-13 - Image Processing
Convolutional Neural Network
Digital image processing book
CNN Additional 1
CNN Additional 2
Assignment Brief Paper
Recording CNN
recording summary image processing
Week 12
Week 13
Weekk #14-15 Text Processing
Recurrent Neural Network
Text Processing
RNN
Additional
Forecasting: Principles and Practice
M Competition
Practical Time Series Forecasting with R - A Hands-On Guide (2024)
The Performance of Ramsey Test, White Test and Terasvirta Test in Detecting Nonlinearity - A Simulation Study
plotXY.R
Simulation study on ESTAR model
Design of Experiment to Optimize the Architecture of Deep Learning for Nonlinear Time Series Forecasting
Pemilihan Arsitektur Terbaik pada Model Deep Learning
SVR for time series
nowcasting: predicting the present
Forecasting with RNN in Intermittent Demand Data
Final Project
Guideline
Submit your progress here
Submission of final project
Guest Lecture
Assignment to Guest Lecture Session
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Course information
Statistical Machine Learning (Q)
Teacher:
Dedy Dwi Prastyo -
Teacher:
Widhianingsih Tintrim Dwi Ary -
Skill Level
:
Beginner
