Experimental Design and AnalysisCovers the basics of experimental design and analysis, focusing on statistical techniques like ANOVA, regression, mediation, and moderation.
Generalized Linear ModelsCovers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.
Probabilistic Linear RegressionExplores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Probabilities and StatisticsCovers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.
Regression: Linear ModelsIntroduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Neural Networks: Multilayer PerceptronsCovers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Probability and StatisticsCovers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.
