Lec 38: Large Language Models (LLMs) and BERT

Lec 36: Ground truth retrieval on synthetic benchmark graphs

Lec 35: Modularity maximization algorithms for community detection

Lec 34: Community detection (clustering network data) and modularity

Lec 33: Network science fundamentals

Lec 37: Network centrality measures and NetworkX

Lec 32: Quantitative reasoning with no information (Fermi estimation)

Lec 31: Quantitative reasoning with incomplete information

Lec 30: A/B testing and Online Controlled Experiments

Lec 29: Controlling the False Discovery Rate FDR using the Benjamini Hochberg procedure

Lec 28: Multiple testing, controlling the family-wise error rate using Bonferroni and Holm methods

Lec 27: Hypothesis testing, type I & type II error, resampling, statistical vs. practical relevance

Lec 26: SVM with radial kernel, SVM for multi class classification, SVM vs logistic regression

Python Lab 9 - Gradient Boosted Trees

Python Lab 8 - Decision Trees and Random Forests

Python Lab 7 - Cross validation for Regression Tasks

Python Lab 6 - Cross-validation for Classification Tasks

Lec 25: Support vector classifier SVC, feature expansion and kernels, support vector machines

Lec 24: Separating hyperplanes and maximum margin classifier

Lec 23: Random forest, gradient boosted trees, and machine learning explainability

Lec 22: Decision tree pruning, bootstrap aggregating (bagging), and bagged trees

Lec 21: Tree based models, decision trees, and regression trees

Lec 20: Local regression and generalized additive model GAM

Lec 19: Non linear models and piecewise polynomial regression

Lec 17: Regularized fitting, ridge and lasso regression

Lec 18: Regression with dimensionality reduction

Lec 16: Cross-validation for model selection

Python Lab 5 - Anomaly Detection GMM

Python Lab 4 - Logistic Regression

Lec 11: Validation and cross-validation

Lec 10: Polynomial regression

Lec 15: Step-wise subset selection

Lec 14: Sparsity and best subset selection

Lec 13: The bootstrap

Lec 12: Leave-one-out cross-validation and train test contamination

Lec 9: Confusion matrix and performance metrics

Lec 8: Discriminant analysis and naïve Bayes

Lec 7: Classification, logistic regression, and confounding

Lec 6: Scalable computing with Dask

Python Lab 3 - kMeans Clustering

Lec 5: Regression with discrete/qualitative variables and interactions

Lec 1: Overfit, underfit, and the bias variance trade-off

Lec 2: Local averaging and the curse of dimensionality

Lec 4: Multiple linear regression

Lec 3: Linear regression

Python Lab 2 - k-Nearest Neighbors Classifier

Python Lab 1 - Machine Learning for Titanic