Artificial Intelligence and Machine Learning

Finding structure in data—clusters, density, and patterns

Understanding why clustering analysis is useful

Mathematical background—distance metrics, machine learning, DBSCAN, HDBSCAN, optics, and more

Feature selection, extraction, and transformation

Singular value decomposition (SVD), independent component analysis, and truncated SVD

Data transformation, projection, and dimension reduction—understanding SVD

Feature embeddings, text transformations, and topic models

Unstructured data concepts—natural language processing fundamentals

High-dimensional data visualization with UMAP and t-SNE

Non-parametric data soothing—Kernel Density Estimation

Notation, training, and model development; loss function; learning as an optimization algorithm

Gradient descent-bayesian networks

Ridge and Lasso

Descriptive classifiers—k-nearest neighbor and Naïve Bayes algorithm

Binary class learning—logistic regression

Hinge, Jacobian, Hessian, and logarithmic loss

Discriminative classifiers vs. descriptive classifiers

Kernel trick and support vector machines

Tree-based prediction algorithms

Extracting classification and regression rules from decision trees

From trees to forests—random forest theoretical approach

Voting classifiers

Bootstrap aggregation (bagging)

Boosting methods

Essentials of model interpretation and the regularization concept in machine learning

Performance evaluation metrics and cross-validation

Generalization error—overfitting vs. underfitting

Dealing with imbalanced data

Anomaly detection realm

Autoencoders—introduction to neural networks

Collaborative filtering

Apriori algorithm

Recommendation systems

Homogenous vs. heterogeneous networks

Graph theory—social network analysis