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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
Testimonials (3)
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
Course - Forecasting with R
The variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
The real life applications using Statcan and CER as examples.
