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