Learning Resources

Text Books :

• Data Mining: Concepts and Techniques, 3E by J. Han et al. Download
• Data Mining: Practical Machine Learning Tools and Techniques, 3E by Ian H. Witten et al. View
• Pattern Recognition and Machine Learning, by Christopher Bishop View

Learn WEKA :

• GUI based learning :
  • by Prof. Ian H. Witten Go YouTube
• Implementation based learning :
  • Implemented in Java by Dr. Noureddin Sadawi Go YouTube, Go Github
  • Implemented in Java by Rafsanjani Muhammod
    • version #01: Go Github & [Romeo Version :):):) -> Section: SA -> June 20, 2017 -> Go Github]
    • version #02: Go Github
  • GUI & Core Java by Rafsanjani Muhammod Go YouTube

Learn scikit-learn :

• Implemented in Python (scikit-learn) by Rafsanjani Muhammod Go Github
• LazyProgrammer Go Github
• Hands-on Machine Learning with Scikit-Learn and TensorFlow Go Github

Blogs :

• An Introduction to Data Mining by Dr. Saed Sayad Go
• Analytics Vidhya Go
• Soft Computing and Intelligent Information Systems Go
• Comparism between classifiers Go, Go

Coming soon … :)

Public datasets for Analytics

• UCI Machine Learning Repository Go
• KEEL Go
• AnalyticsVidhya Go
• Kaggle (This is mainly a contest site.) Go
• Public datasets for Machine Learning Go
• Algorithmia Go
• Springboar Go

Syllabus

Key Terms :

1. Features / Attributres
2. Feature-values & Attributre-values
3. Class & Class-Attributes
4. Instances / Records / Vectors / Tuples
5. Two-class dataset & Multi-class dataset/Multi-label datasets (when number of class-values is gretter than 2.)
6. High-dimensional (When number of feature is gretter than 10)
7. Univariate, Bivariate & Multivariate dataset Go, Go
8. Balanced dataset vs Imbalanced dataset
9. Overfitting & Underfitting of a dataset
10. Supervised learning vs. Unsupervised learning Go
11. Classification, Regression, Clustering
12. Bias–variance tradeoff Go
13. Noisy Datasets & how to remove noise ?
14. Anomaly Detection Go

Preprocessing Datasets :

• Data cleaning Go
• Remove duplicate elements
• Handle missing elements (Can you calculate : Mean, Median, Mode, Standard Deviation etc.?)
• Feature Scaling or Feature Normalization (Can you calculate distance using : Euclid, Manhattan, Minkowski etc.?)

Classification :

• Rule Classifiers
  • ZeroR Classifier
  • OneR Classifier Go & Go
• Logistic Regression
• KNN Classifier
• Support Vector Classifier ( Kernels : Linear, Polynomial, Gaussian, Sigmoid, etc. )
• Naive Bayes Classifier
• Decision Tree Classifier
  • Gini
  • ID3
  • C4.5 / C5.0 / J48
• Ensemble Learning
  • Bagging Classifier
  • Boosting Classifier (AdaBoost, Gradient Boosting)
  • Random Forest Classifier
• Introduction to Deep Learning (ANN, RNN, CNN, SOM, Autoencoders )

Regression :

• Linear Regression (Simple & Multiple)
• Polynomial Regression
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression

Clustering :

• KMeans
• Hierarchical (Agglomerative, Divisive)

Imbalanced Learning : Go

• Majority class vs Minority class
• Re-sampling : Over-sampling, Under-sampling
  • Over-sampling algorithms : ADASYN, SMOTE, Random Over-sampling
  • Under-sampling algorithms : Random Under-sampling

Features Selection :

• Filter Approach (LDA)
• Wrapper Approach

Features Generation:

• Embeded Approach
• Principal Component Analysis (PCA)

Performance Measures : Go

• Understand Confusion Matrix
• Calculate : Accuracy, Error, Sensitivity, Specificity, Precision, Recall
• ROC Curve & AUPR Curve

Course Materials

• Naive-Bayes Download
• Association Rules Mining Download

Course Schedule

• Week #1 :
  • Introduction to Pattern Recognition,
  • Current Researh Trend,
  • Introduction to WEKA
• Week #2 :
  • Hands-on practice on WEKA GUI
    • Understand what are the .CSV & .ARFF file
    • Data Vizualization
    • Classifier design
    • Use different machine learning algorithms
  • Evaluation options
    • use training dataset
    • supplied test dataset
    • cross-validation (KFold=10)
    • split dataset (2/3 train & 1/3 test)
  • Confusion Matrix
    • TP, FP, FN, FP
    • Performance Measure : Accuracy, Error, TPR, FPR, F-Score etc.
    • weighted mean
  • Assignment #1 : Choose all (25) datasets from WEKA & submit report on it (Week #03).
• Week #3 :
  • Introduction to WEKA implementation in Java.
    • Assignment #2 : Two huge datasets provides & submit report on it (Week #04)
• Week #4 :
  • Details on WEKA implementation in Java.
    • Actual vs Prediction
    • Evaluation options
    • Feature Reduction
• Week #5 : Ensemble Learning
• Week #6 : Midterm Exam (Classifiers : based on your both Lab & Theory courses.)
• Week #7 : Clustering
• Week #8 : Data Analysis with scikit-learn (Python)-I
  • Loading the datasets (using pandas, numpy)
  • Features scalling
  • Machine Learning Classifiers
  • Evaluation Matrix
  • Assignment #3 : Datasets will provide.
• Week #9 : Data Analysis with scikit-learn (Python)-II
  • Problem solving
  • Draw curve on scikit-learn (eg. ROC Curve, AUC Curve)
  • More tricks (imblearn)
• Week #10 : Data Analysis with scikit-learn (Python)-III
  • Introduction to Kaggle competetion Go
  • Problem solving
  • Assignment #4 : A dataset will provide.
• Week #11 : Presentation based on datasets. (Individual)
  • Sample Slide: KDD’99 Datasets &
  • Sample Slide: Analysis of the Datasets
• Week #12 : Final Exam.