Machine Learning Resources

Resources

• Causal Inference for the Brave and True
• Rob J Hyndman and George Athanasopoulos. Forecasting: Principles and Practice
• Scott Cunningham. Causal Inference: The Mixtape
• Mastering Metrics

Courses

• Machine Learning Specialization. Coursera: Stanford University and DeepLearning.AI
• Deep Learning Specialization. Coursera: DeepLearning.AI

Newsletters

• The Batch: DeepLearning.AI.
• OpenAI Dev Digest.

Books

• Hands-On Machine Learning with Scikit-Learn & TensorFlow.
• The Master Algorithm.
• Mathematics for Machine Learning
• Machine Learning Design Patterns
• Introduction to Statistical Learning
• Clean Code
• The Art of Statistics. David Spiegelhater
• Effective data storytelling. Brent Dykes

Econometrics

• Joshua Angrist. Mostly Harmless Econometrics
• Greene. “Econometric Analysis”
• Wooldridge. “Analysis of Cross Section and Panel Data”

Glossary

AD Autodiff (automatic differentiation)

ML Machine Learning

DL Deep learning

BERT Bidirectional Encoder Representations from Transformers

NLP Natural Language Processing

NER Named Entity Recognition

QA Question Answerings

GPT Generative Pre-trained Transformer

GBDT Gradient-boosted decision trees

XGBoost eXtreme Gradient Boosting

SVM Support Vector Machine

GRU Gated Recurrent Unit

LSTM Long Short-Term Memory

MFCC Mel Frequency Cepstral Coefficients

LRN Local Response Normalization

RBM Restricted Boltzmann machine

EDA Exploratory Data Analysis

ETL Extract, Transform, Load

MARS Multivariate Adaptive Regression Splines

GAN Generative Adversarial Networks

LGBM Light gradient-boosting machine

RF Random Forest

KDE Kernel Density Estimate

DBSCAN Density-based spatial clustering of applications with noise

BIRCH Balance Iterative Reducing and Clustering using Hierarchies

OPTICS Ordering Points to Identify the Clustering Structure

RFM Recency, Frequency, Monetary model

NPS Net Promoter Score

LoRA Low-Rank-Adaption of Large Language Model

LLM Large Language Model

PEFT Parameter-efficient fine-tuning

ARL Association Rule Learning

GLM Generalized Linear Model

Visualization

PCA Principal Component Analysis

t-SNE T-Distributed stochastic neighbor embedding (also TSNE)

LDA Linear Discriminant Analysis

UMAP Uniform Manifold Approximation and Projection

Neural Networks

BRNN Bidirectional Recurrent Neural Network

RNN Recurrent Neural Network

CNN Convolutional Neural Network

DNN Deep Neural Network

R-CNN Region Based Convolutional Neural Network

Metrics

ROC (ROC curve) Receiver Operating Characteristic curve. Plots sensitivity vs. 1-specificity (TPR vs. FPR)

AUC Area under the ROC curve. Source: Google Course.

TPR True Positive Rate $\text{TPR}=\frac{tp}{tp+fn}$ (Recall, sensitivity)

FPR False Positive Rate $\text{FPR}=\frac{fp}{fp+tn}=1-\text{TNR}$ (fall-out)

TNR True Negative Rate: specificity

Time Series

STL Seasonal and Trend decomposition using Loess

ARIMA Autoregressive integrated moving average

SVD Singular Value Decomposition

UCM Unobserved Components Model