SUMMARY

1. Apps - Multicriteria Decision Aids - Java
2. Apps - Operations Research - Java
3. Data Science - Association Rules - Python
4. Data Science - Classification Algorithms - Python
5. Data Science - Decision Trees - Python
6. Data Science - Recommender Systems - Python
7. Data Science - Natural Language Processing - Python
8. Data Science - Neural Networks - Python
9. Forecasting - Python
10. Metaheuristics - Classical - Python
11. Metaheuristics - Discrete - Python
12. Metaheuristics - Multiobjective - Python
13. Metaheuristics - Nature Inspired - Python
14. Multivariate Data Analysis - R and SPSS
15. Others - Python

Apps - Multicriteria Decision Aids - Java

• J-ELECTRE (ELimination and Choice Expressing REality) : Electre I, I_s, I_v, II, III, IV, TRI and TRI-ME Software. Lessons Included.
• Voracious-AHP (Analytic Hierarquic Process) : A software that can remove or reduce the inconsistency from an AHP Matrix. Lessons Included.

Apps - Operations Research - Java

• J-Horizon : A Vehicle Routing Problem Software. CVRP (Capacitated VRP), MDVRP (Multiple Depot VRP), VRPTW (VRP with Time Windows), VRPB (VRP with Backhauls), VRPPD (VRP with Pickups and Deliveries), VRP with Homogeneous or Heterogeneous Fleet, TSP, mTSP and various combination of these types
• J-EOQ-SA : EOQ (Economic Order Quantity) for a single product with No Discounts, All Units or Incremental Discounts and with or without Backorders

Data Science - Association Rules - Python

• Apriori Algorithm : Apriori Algorithm - An Association Rule Learning Over Transactions Databases

Data Science - Classification Algorithms - Python

• Naive Baye Classifier : Naive Bayes Classifier for Supervised Learning Problems

Data Science - Decision Trees - Python

• ID3 (Iterative Dichotomiser 3) : ID3 Algorithm - A Decision Tree for Categorical Data with Pruning Methods
• C4.5 : C4.5 Algorithm - A Decision Tree for Numerical and Categorical Data that can Handle Missing Values and Pruning Methods
• CART (Classification And Regression Trees) : CART Algorithm - A Decision Tree for Numerical and Categorical Data that can Handle Missing Values and Pruning Methods
• Random Forest : Random Forest Algorithm - A Decision Tree Ensemble for Numerical and Categorical Data that can Handle Missing Values

Data Science - Recommender Systems - Python

• CBF (Content Based Filtering) : Content-Based Filtering using TF-IDF Matrices with Cosine Similarity
• CF Item (Collaborative Filtering - Item Based) : Collaborative Filtering Function using an Item Based Regression Approach
• CF User (Collaborative Filtering - User Based) : Collaborative Filtering Function using an User Based Regression Approach
• CF User-Item (Collaborative Filtering - User & Item Based) : Collaborative Filtering Function using an User-Item Based Regression Approach
• CF Latent Factors (Collaborative Filtering - Latent Factors) : Collaborative Filtering Function using Regression with Latent Factors Approach
• CF Nearest Neighbors (Collaborative Filtering - Nearest Neighbors) : Collaborative Filtering Function using a Nearest Neighbors Approach
• CF SVD (Collaborative Filtering - SVD) : Collaborative Filtering Function using a SVD (Singular Value Decomposition) Approach

Data Science - Natural Language Processing - Python

• LDA (Latent Dirichlet Allocation) : Latent Dirichlet Allocation (LDA) function. Also computes the dtm, binary dtm, tf dtm and tf-idf dtm

Data Science - Neural Networks - Python

• CNN (Convolutional Neural Network) : SLR
• Neural Network : Pure Python Neural Network Function for Binary or Linear Problems

Forecasting - Python

• Forecasting Lessons :
  • Lesson 01 - Introduction to Forecasting
  • Lesson 02 - Time Series Decomposition
  • Lesson 03 - Holt's Method
  • Lesson 04 - Holt-Winters' Method
  • Lesson 05 - Multiple Linear Regression
  • Lesson 06 - Logistic Regression
• Moving Averages : Calculates the Centered Moving Average (Weighted, Simple or Exponential) of a Time Series
• Decomposition : Decomposition of Timeseries Using the X-11 Algorithm
• Holt Method : Calculates the Additive or Multiplicative Holt's Method for Time Series with Trend
• Holt-Winters Method : Calculates the Additive or Multiplicative Holt-Winters' Method for Time Series with Trend and Seasonality

Metaheuristics - Classical - Python

• ARS (Adaptive Random Search) : Adaptive Random Search to Minimize Functions with Continuous Variables
• CEM (Cross Entropy Method) : Cross Entropy Method (or Kullback–Leibler divergence) to Minimize Functions with Continuous Variables
• DE (Differential Evolution) : Differential Evolution to Minimize Functions with Continuous Variables
• GA (Genetic Algorithm) : Genetic Algorithm to Minimize Functions with Continuous Variables
• MA (Memetic Algorithm) : Memetic Algorithm with Lamarckian Learning (xhc - Crossover Hill Climbing) to Minimize Functions with Continuous Variables. Real Values Encoding.
• RS (Random Search) : Random Search to Minimize Functions with Continuous Variables
• SA (Simulated Annealing) : Simulated Annealing to Minimize Functions with Continuous Variables

Metaheuristics - Discrete - Python

• 2-opt : 2-opt Function for TSP problems
• 2-opt Stochastic : 2-opt Stochastic Function for TSP problems
• 3-opt : 3-opt Function for TSP problems
• 4-opt : 4-opt Function for TSP problems
• EO (Extremal Optimization) : Extremal Optimization Function for TSP problems
• GRASP (Greedy Randomized Adaptive Search Procedure) : Greedy Randomized Adaptive Search Procedure Function for TSP problems.
• GS (Guided Search) : Guided Search Function for TSP problems
• IS (Iterated Search): Iterated Search Function for TSP problems
• SS (Scatter Search) : Scatter Search Function for TSP problems
• TS (Tabu Search) : Tabu Search Function for TSP problems
• VNS (Variable Neighborhood Search) : Variable Neighborhood Search Function for TSP problems

Metaheuristics - Multiobjective - Python

• NSGA II (Non-Dominated Sorting Genetic Algorithm II) : NSGA II (Non-Dominated Sorting Genetic Algorithm II) Function to Minimize Multiple Objectives with Continuous Variables. Real Values Encoded
• SPEA 2 Strength Pareto Evolutionary Algorithm 2 : SPEA 2 (Strength Pareto Evolutionary Algorithm 2) Function to Minimize Multiple Objectives with Continuous Variables. Real Values Encoded

Metaheuristics - Nature Inspired - Python

• ACO (Ant Colony Optimization) : Ant Colony Optimization Function for TSP problems
• ABC (Artificial Bee Colony) : Artificial Bee Colony Optimization to Minimize Functions with Continuous Variables
• ALO (Ant Lion Optimizer) : Ant Lion Optimizer to Minimize Functions with Continuous Variables
• BTA (Bat Algorithm) : Bat Algorithm to Minimize Functions with Continuous Variables
• CKS (Cuckoo Search) : Cuckoo Search to Minimize Functions with Continuous Variables
• DFO (Dispersive Flies Optimization) : Dispersive Flies Optimization to Minimize Functions with Continuous Variables
• FFA (Firefly Algorithm) : Firefly Algorithm to Minimize Functions with Continuous Variables
• FPA (Flower Pollination Algorithm) : Flower Pollination Algorithm to Minimize Functions with Continuous Variables
• GWO (Grey Wolf Optimizer)) : Grey Wolf Optimizer to Minimize Functions with Continuous Variables
• MFA (Moth Flame Algorithm) : Moth Flame Algorithm to Minimize Functions with Continuous Variables
• MVO (Multi Verse Optimizer) : Multi-Verse Optimizer to Minimize Functions with Continuous Variables
• PSO (Particle Swarm Optimization) : Paticle Swarm Optimization to Minimize Functions with Continuous Variables
• SCA (Sine Cosine Algorithm) : Sine Cosine Algorithm to Minimize Functions with Continuous Variables
• SSA (Salp Swarm Algorithm) : Salp Swarm Algorithm to Minimize Functions with Continuous Variables
• WOA (Whale Optimization Algorithm) : Whale Optimization Algorithm to Minimize Functions with Continuous Variables

Multivariate Data Analysis - R and SPSS

• MVDA Lessons (R) - Codes in R available :

  • Lesson 03 - Exploratory Factor Analysis
  • Lesson 04 - Multidimensional Scaling
  • Lesson 05 - Correspondence Analysis
  • Lesson 06 - Discriminant Analysis
  • Lesson 07 - Multiple Linear Regression
  • Lesson 08 - Logistic Regression (Binary)
  • Lesson 09 - Logistic Regression (Multinomial)
  • Lesson 10 - Confirmatory Factor Analysis
  • Lesson 11 - Canonical Correlation

• MVDA Lessons (SPSS) :

  • Lesson 01 - Introduction
  • Lesson 02 - Scales & Descriptive Statistics
  • Lesson 03 - Exploratory Factor Analysis
  • Lesson 04 - Multidimensional Scaling
  • Lesson 05 - Correspondence Analysis
  • Lesson 06 - Discriminant Analysis
  • Lesson 07 - Multiple Linear Regression
  • Lesson 08 - Logistic Regression (Binary)
  • Lesson 09 - Logistic Regression (Multinomial)
  • Lesson 10 - Confirmatory Factor Analysis
  • Lesson 11 - Canonical Correlation

Others - Python

• LUDPP (LU Decomposition with Partial Pivoting) : Lower–Upper Decomposition with Partial Pivoting