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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.
