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Machine Learning Concepts List

This document contains 200 concepts for the Machine Learning: Algorithms and Applications course.

Concepts (1-200)

  1. Machine Learning
  2. Supervised Learning
  3. Unsupervised Learning
  4. Classification
  5. Regression
  6. Training Data
  7. Test Data
  8. Validation Data
  9. Feature
  10. Label
  11. Instance
  12. Feature Vector
  13. Model
  14. Algorithm
  15. Hyperparameter
  16. K-Nearest Neighbors
  17. Distance Metric
  18. Euclidean Distance
  19. Manhattan Distance
  20. K Selection
  21. Decision Boundary
  22. Voronoi Diagram
  23. Curse of Dimensionality
  24. KNN for Classification
  25. KNN for Regression
  26. Lazy Learning
  27. Decision Tree
  28. Tree Node
  29. Leaf Node
  30. Splitting Criterion
  31. Entropy
  32. Information Gain
  33. Gini Impurity
  34. Pruning
  35. Overfitting
  36. Underfitting
  37. Tree Depth
  38. Categorical Features
  39. Continuous Features
  40. Feature Space Partitioning
  41. Logistic Regression
  42. Sigmoid Function
  43. Log-Loss
  44. Binary Classification
  45. Multiclass Classification
  46. Maximum Likelihood
  47. One-vs-All
  48. One-vs-One
  49. Softmax Function
  50. Regularization
  51. L1 Regularization
  52. L2 Regularization
  53. Ridge Regression
  54. Lasso Regression
  55. Support Vector Machine
  56. Hyperplane
  57. Margin
  58. Support Vectors
  59. Margin Maximization
  60. Hard Margin SVM
  61. Soft Margin SVM
  62. Slack Variables
  63. Kernel Trick
  64. Linear Kernel
  65. Polynomial Kernel
  66. Radial Basis Function
  67. Gaussian Kernel
  68. Dual Formulation
  69. Primal Formulation
  70. K-Means Clustering
  71. Centroid
  72. Cluster Assignment
  73. Cluster Update
  74. K-Means Initialization
  75. Random Initialization
  76. K-Means++ Initialization
  77. Elbow Method
  78. Silhouette Score
  79. Within-Cluster Variance
  80. Convergence Criteria
  81. Inertia
  82. Neural Network
  83. Artificial Neuron
  84. Perceptron
  85. Activation Function
  86. ReLU
  87. Tanh
  88. Sigmoid Activation
  89. Leaky ReLU
  90. Weights
  91. Bias
  92. Forward Propagation
  93. Backpropagation
  94. Gradient Descent
  95. Stochastic Gradient Descent
  96. Mini-Batch Gradient Descent
  97. Learning Rate
  98. Loss Function
  99. Mean Squared Error
  100. Cross-Entropy Loss
  101. Epoch
  102. Batch Size
  103. Vanishing Gradient
  104. Exploding Gradient
  105. Weight Initialization
  106. Xavier Initialization
  107. He Initialization
  108. Fully Connected Layer
  109. Hidden Layer
  110. Output Layer
  111. Input Layer
  112. Network Architecture
  113. Deep Learning
  114. Multilayer Perceptron
  115. Universal Approximation
  116. Convolutional Neural Network
  117. Convolution Operation
  118. Filter
  119. Kernel Size
  120. Stride
  121. Padding
  122. Valid Padding
  123. Same Padding
  124. Feature Map
  125. Receptive Field
  126. Pooling Layer
  127. Max Pooling
  128. Average Pooling
  129. Spatial Hierarchies
  130. Translation Invariance
  131. Local Connectivity
  132. Weight Sharing
  133. CNN Architecture
  134. LeNet
  135. AlexNet
  136. VGG
  137. ResNet
  138. Inception
  139. Transfer Learning
  140. Pre-Trained Model
  141. Fine-Tuning
  142. Feature Extraction
  143. Domain Adaptation
  144. ImageNet
  145. Model Zoo
  146. Freezing Layers
  147. Learning Rate Scheduling
  148. Bias-Variance Tradeoff
  149. Training Error
  150. Validation Error
  151. Test Error
  152. Generalization
  153. Cross-Validation
  154. K-Fold Cross-Validation
  155. Stratified Sampling
  156. Holdout Method
  157. Confusion Matrix
  158. True Positive
  159. False Positive
  160. True Negative
  161. False Negative
  162. Accuracy
  163. Precision
  164. Recall
  165. F1 Score
  166. ROC Curve
  167. AUC
  168. Sensitivity
  169. Specificity
  170. Data Preprocessing
  171. Normalization
  172. Standardization
  173. Min-Max Scaling
  174. Z-Score Normalization
  175. One-Hot Encoding
  176. Label Encoding
  177. Feature Engineering
  178. Feature Selection
  179. Dimensionality Reduction
  180. Data Augmentation
  181. Computational Complexity
  182. Time Complexity
  183. Space Complexity
  184. Scalability
  185. Batch Processing
  186. Online Learning
  187. Optimizer
  188. Adam Optimizer
  189. RMSprop
  190. Momentum
  191. Nesterov Momentum
  192. Gradient Clipping
  193. Dropout
  194. Early Stopping
  195. Model Evaluation
  196. Model Selection
  197. Hyperparameter Tuning
  198. Grid Search
  199. Random Search
  200. Bayesian Optimization