Models and algorithms for Computer Engineering students
Main topics:
- Clustering
- Regression
- Classification
- Biologicaly Inspired Algorithms
- Machine Learning (ML)
- Supervised
- Unsupervised
Programming Languages:
- Java
- Python
- Go
- C
Basic process:
- Analysis (scope definition, questions, delimitation)
- Mining (Get, clean, and combine data)
- Explore (Understanding)
- Model (Create computational, math, statistical model
- Visualize (Communicate, create algorithms, publish app or WEB API)
List of algorithms and models:
- A*
- Analysis of variance (ANOVA)
- Arbitrage
- Artificial Neural Networks
- Association Rules
- Attribution Modeling
- B-Tree, B+Tree
- Bayesian Statistics
- Bezier Curve
- Binary Tree
- Clustering
- Collaborative Filtering
- Comutação Evolutiva
- Confidence Interval
- Conjoint Analysis
- Convolution
- Cross-Validation
- Curve fitting (Ajuste de curvas)
- Decision Trees
- Deep Learning
- Density Estimation
- Dijkstra’s algorithm
- Ensemble Methods
- Ensembles
- Experimental Design
- Factor analysis
- Factorization
- Feature Selection
- Fourier (FFT)
- Game Theory
- Geo-X (Spatial Modeling)
- Graphs
- Hypothesis Testing
- Imputation
- Interpolation
- Indexation/Cataloguing
- Jackknife Regression
- Least squares
- Lift Modeling
- Linear Regression
- Linkage Analysis
- Logistic Regression
- Machine Learning
- Minimax
- Model Fitting
- Monte Carlo
- Naive Bayes
- Natural Language Processing (NLP)
- Nearest Neighbors (k-NN)
- Newton Raphson
- Pattern Recognition
- Predictive Modeling
- Principal Component Analysis
- Principal Component Analysis (PCA)
- Random Forest
- Random Numbers
- Recommendation Engine
- Relevancy Algorithm
- Rule System
- Scoring Engine
- Search Engine
- Segmentation
- Sort Algotithms
- Quick Sort
- Bubble Sort
- Radix Sort
- Insertion Sort
- Spline
- Support Vector Machine (SVM)
- Survival Analysis
- Swarm Intelligence
- Test of Hypotheses
- Time Series
- XGBoost/LightGBM
- Yield Optimization
Reference:
- Introduction to Algorithms, MIT Press, Cormen, Et al.
- https://en.wikipedia.org/wiki/List_of_algorithms
- https://www.datasciencecentral.com/40-techniques-used-by-data-scientists/
- https://www.techtarget.com/searchbusinessanalytics/feature/15-common-data-science-techniques-to-know-and-use
- https://www.java67.com/2018/06/data-structure-and-algorithm-interview-questions-programmers.html
- 40 Algorithms Every Programmer Should Know: Hone your problem-solving skills by learning different algorithms and their implementation in Python by Imran Ahmad