ML Algorithms that we use in present days.

1. Machine Learning
Machine Learning Algorithms
Elias Hasnat | 2017-8-31

1 / 14

2. Bayesian
1. Naive Bayes
2. Gaussian Naive Bayes
3. Multinomial Naive Bayes
4. Average One-Dependence Estimators (AODE)
5. Bayesian Belief Network (BBN)
6. Bayesian Network (BN)
7. Hidden Markov Model (HMM)
8. Markov Chain
9. Monte Carlo Simulation
10. Conditional Random Fields (CRF)
2 / 14

3. Decision Tree
1. Classi cation and Regression Tree (CART)
2. Interactive Dichotomiser 3 (ID3)
3. C4.5 and C5.0
4. Chi-Squared Automatic Interaction Detection (CHAID)
5. Decision Stump
6. M5
7. Random Forests
8. Conditional Decision Trees

3 / 14

4. Clustering
1. Single Linkage Clustering
2. K-Means
3. K-Medians
4. Expectation maximization (EM)
5. Hierarchical Clustering
6. DBSCAN
7. OPTICS Algorithm
8. Non Negative Matrix Factorization
9. Latent Dirichlet Allocation (LDA)

4 / 14

5. Neural Networks
1. Self Organizing Map (SOM)
2. Perceptron
3. Back Propagation
4. Hop eld Network
5. Radial Basis Function Network (RBFN)
6. Back Propagation
7. Auto Encoders

5 / 14

6. Neural Networks (cntd.)
1. Hop eld Networks
2. Boltzman Machines
3. Restricted Boltzman Machine
4. Spiking Neural Network
5. Learning Vector Quantization (LVQ)

6 / 14

7. Ensemble
1. Boosting (Logit Bost)
2. Bootstrapped Aggregation (Bagging)
3. AdaBoost
4. Stacked Generalization (Blending)
5. Gradient Boosting Machine (GBM)
6. Gradient Boosting Regression Trees (GBRT)
7. Random Forest

7 / 14

8. Association Rule
1. Apriori
2. Eclat
3. FP-Growth

8 / 14

9. Deep Learning
1. Deep Boltzman Machine (DBM)
2. Deep Belief Network (DBN)
3. Convolution Neural Network (CNN)
4. Generative Adverserial Network (GAN)
5. Stacked Auto Encoders

9 / 14

10. Dimensionality Reduction
1. Principal Component Analysis (PCA)
2. Principal Component Regression (PCR)
3. Partial Least Square Regression (PLSR)
4. Sammon Mapping
5. Multidimentional Scalling (MDS)
6. Projection Persuit
7. Discriminant Analysis (LDA, MDA, QDA, FDA)

10 / 14

11. Instance Based
Cake Based or Memory Based
1. k-Nearest Neighbours (kNN)
2. Learning Vector Quantization (LVQ)
3. Self Organizing Map (SOM)
4. Locally Wighted Learning (LWL)

11 / 14

12. Regularization
1. Ridge Regression
2. Least Absolute Shrinkage and Selection Operation (LASSO)
3. Elastic Net
4. Least-Angle Regression (LARS)

12 / 14

13. Regression
1. Ordinary Least Squares Regression (OLSR)
2. Linear Regression
3. Logistic Regression
4. Stepwise Regression
5. Multivariate Adaptive Regression Splines (MARS)
6. Locally Estimated Scatterplot Smoothing (LOESS)
7. Jckknife Regression

13 / 14

14. Thank You

14 / 14