Reverse Chronological

• Power Analysis
• Anomaly Detection Basics
• Recommendation System Fundamentals
• Elimination with matrices
• [Paper] RankNet to LambdaRank to LambdaMART
• On Interleaving
• [Paper] Semantic Product Search
• Exponential, Poisson and Gamma Distribution
• Quantile Function (Inverse CDF)
• Gradient Boosting
• Central limit theorem
• Types of Statistical Studies
• Reservoir sampling
• log-linear and log-log regression
• From highscalability.com
• KL Divergence
• IR metrics
• Geometry of Linear Equations – Column Picture
• Correlation and Regression Slope
• Softmax and cross entropy Loss
• VIF and Multicollinearity
• Clustering Metrics
• Oversampling and Under-sampling
• Chi Square Test
• Generative and Discriminative Models
• Contrast Analysis
• NN : Batch Norm and Softmax Regression
• Optimization for NN
• Inverted Dropout
• Hidden Markov Models
• Hierarchical Clustering
• Taylor Series Expansion
• Which ML algorithm to use when?
• Matrices Definitions
• Principal Component Analysis (PCA)
• Naive Bayes Classifier
• ANOVA Introduction
• Linear Regression Derivations
• Bayesian Learning – Quick Summary
• Probabilistic Clustering
• K-Means Clustering
• Nearest Neighbour Search
• Overfitting in Decision Trees
• Handling missing values in Decision Tree
• On Classification Accuracy – 2
• Step Size in Descent Methods
• knn and kernel regression
• Generalized Linear Models (GLM)
• Exponential Family
• Subgradient Methods
• Interpretation of Multiple Regression Coefficients
• Grubb’s Test for Anomaly Detection
• Ensemble, Bagging and Boosting
• AdaBoost
• IID Assumption
• Panel Data And Analysis
• Gradient Descent vs Netwon’s Method
• Regression Trees
• Classification Trees
• Q-Learning
• Simple Linear Program
• ARIMA model
• Fitting AR Processes
• Moving average and Auto-regressive Processes
• Stationarity Conditions for MA(q) and AR(p) Processes
• [Time Series] Correlation and Stationarity
• Iterative Method for Unconstrained Optimization
• Time series week 1
• On Clustering
• No Free Lunch (NFL)
• Negative sampling in word2vec
• Word2Vec and skip gram model
• Quadratic Programming CVXOPT
• Derivation of backpropogation
• Derivation Of Backpropagation – 2
• SVM Solution Lagrange
• Dynamic Programming for RL
• OpenAI Gym Environment
• [Example] Lagrange Multiplier With Equality Constraints
• Thompson Sampling
• Deep learning taking off
• Lagrange Duality
• Parametric and Nonparametric tests
• Class 12 Geometry Notes
• [Theory] Lagrange Multiplier and Constrained Optimization
• On multivariate Gaussian
• Probability Rules and Tricky Questions
• Support Vector Machines
• Comparing LDA and LR
• On LDA, QDA
• Classification – One vs Rest and One vs One
• On Classification Accuracy
• Cost Function And Hypothesis for Logistic Regression
• Hypothesis and T-Distribution
• Q – Q Plots
• Probability Distribution
• Interpreting Statistical Values

Chronological

• Interpreting Statistical Values
• Probability Distribution
• Q – Q Plots
• Hypothesis and T-Distribution
• Cost Function And Hypothesis for Logistic Regression
• On Classification Accuracy
• Classification – One vs Rest and One vs One
• On LDA, QDA
• Comparing LDA and LR
• Support Vector Machines
• Probability Rules and Tricky Questions
• On multivariate Gaussian
• [Theory] Lagrange Multiplier and Constrained Optimization
• Class 12 Geometry Notes
• Parametric and Nonparametric tests
• Lagrange Duality
• Deep learning taking off
• Thompson Sampling
• [Example] Lagrange Multiplier With Equality Constraints
• OpenAI Gym Environment
• Dynamic Programming for RL
• SVM Solution Lagrange
• Derivation Of Backpropagation – 2
• Derivation of backpropogation
• Quadratic Programming CVXOPT
• Word2Vec and skip gram model
• Negative sampling in word2vec
• No Free Lunch (NFL)
• On Clustering
• Time series week 1
• Iterative Method for Unconstrained Optimization
• [Time Series] Correlation and Stationarity
• Stationarity Conditions for MA(q) and AR(p) Processes
• Moving average and Auto-regressive Processes
• Fitting AR Processes
• ARIMA model
• Simple Linear Program
• Q-Learning
• Classification Trees
• Regression Trees
• Gradient Descent vs Netwon’s Method
• Panel Data And Analysis
• IID Assumption
• AdaBoost
• Ensemble, Bagging and Boosting
• Grubb’s Test for Anomaly Detection
• Interpretation of Multiple Regression Coefficients
• Subgradient Methods
• Exponential Family
• Generalized Linear Models (GLM)
• knn and kernel regression
• Step Size in Descent Methods
• On Classification Accuracy – 2
• Handling missing values in Decision Tree
• Overfitting in Decision Trees
• Nearest Neighbour Search
• K-Means Clustering
• Probabilistic Clustering
• Bayesian Learning – Quick Summary
• Linear Regression Derivations
• ANOVA Introduction
• Naive Bayes Classifier
• Principal Component Analysis (PCA)
• Matrices Definitions
• Which ML algorithm to use when?
• Taylor Series Expansion
• Hierarchical Clustering
• Hidden Markov Models
• Inverted Dropout
• Optimization for NN
• NN : Batch Norm and Softmax Regression
• Contrast Analysis
• Generative and Discriminative Models
• Chi Square Test
• Oversampling and Under-sampling
• Clustering Metrics
• VIF and Multicollinearity
• Softmax and cross entropy Loss
• Correlation and Regression Slope
• Geometry of Linear Equations – Column Picture
• IR metrics
• KL Divergence
• From highscalability.com
• log-linear and log-log regression
• Reservoir sampling
• Types of Statistical Studies
• Central limit theorem
• Gradient Boosting
• Quantile Function (Inverse CDF)
• Exponential, Poisson and Gamma Distribution
• [Paper] Semantic Product Search
• On Interleaving
• [Paper] RankNet to LambdaRank to LambdaMART
• Elimination with matrices
• Recommendation System Fundamentals
• Anomaly Detection Basics
• Power Analysis