domains/machine-learning-textbook/learning-graph.csv

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