Course Outline

Foundations of Machine Learning

  • Introduction to Machine Learning concepts and workflows
  • Supervised vs. unsupervised learning
  • Evaluating machine learning models: metrics and techniques

Bayesian Methods

  • Naive Bayes and multinomial models
  • Bayesian categorical data analysis
  • Bayesian graphical models

Regression Techniques

  • Linear regression
  • Logistic regression
  • Generalized Linear Models (GLM)
  • Mixed models and additive models

Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Factor Analysis (FA)
  • Independent Component Analysis (ICA)

Classification Methods

  • K-Nearest Neighbors (KNN)
  • Support Vector Machines (SVM) for regression and classification
  • Boosting and ensemble models

Neural Networks

  • Introduction to neural networks
  • Applications of deep learning in classification and regression
  • Training and tuning neural networks

Advanced Algorithms and Models

  • Hidden Markov Models (HMM)
  • State Space Models
  • EM Algorithm

Clustering Techniques

  • Introduction to clustering and unsupervised learning
  • Popular clustering algorithms: K-Means, Hierarchical Clustering
  • Use cases and practical applications of clustering

Summary and Next Steps

Requirements

  • Basic understanding of statistics and data analysis
  • Programming experience in R, Python, or other relevant programming languages

Audience

  • Data scientists
  • Statisticians
 14 Hours

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