Tags
Data Science
Published
Sep 1, 2022
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Machine Learning Notes
#1: Components of a Learning System
Sep 2, 2021 06:34 PM
#2: Feasibility of Learning
Sep 7, 2021 06:02 PM
#3: Linear Regression, Regularization, Bias-Variance
Sep 9, 2021 06:36 PM
#4: Logistic Regression & Gradient Descent
Sep 14, 2021 04:39 PM
#5-6: Bayesian Machine Learning
Sep 21, 2021 05:49 PM
#7: SVM, Optimization
Oct 5, 2021 06:08 PM
Dual Form Solution, Kernel Trick, Hinge Loss
#8: Neural Networks
Oct 19, 2021 06:24 PM
XOR, Backpropagation Algorithm
#9: CNN & RNN
Oct 26, 2021 06:41 PM
+ LSTM
#10: Decision Trees
Nov 2, 2021 06:10 PM
+ Random Forest
#11: Ensemble Learning
Nov 9, 2021 07:18 PM
Bagging & Boosting
#12: Dimensionally Reduction
Nov 16, 2021 07:10 PM
Dimensionally Reduction, PCA, Matrix Factorization
#13-14: Clustering
Nov 30, 2021 07:14 PM
Hierarchical Clustering, Flat Clustering/KMeans, Gaussian Mixture Model, Expectation Maximization Algorithm
#15: Parametric Methods
Dec 9, 2021 06:25 PM
Density Estimation, KNN, Gaussian Process
#16: Reinforcement Learning
Dec 9, 2021 07:19 PM