Lectures related to Developing a Model

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Introduction to Behavior Modeling: Simple Example

Introduces behavior modeling through a simple example, focusing on choice modeling components and the analysis of the electric car market.

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Traffic flow modeling: Developing and testing traffic models MOOC: Intro to Traffic Flow Modeling and Intelligent Transport Systems

Covers the fundamentals of traffic flow modeling, including model classification and testing techniques.

Hidden Markov Models (HMM): Theory

Covers Hidden Markov Models (HMM) for modeling time series data and decoding using the Viterbi Algorithm.

Developing a Model: Simple Example MOOC: Introduction to Discrete Choice Models

Presents a simple example of developing a model with a focus on variables and causal relationships.

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Causal Inference: Understanding Associations and Effects

Explores the distinction between association and causation in statistical analysis, highlighting the limitations of association in inferring causation.

AdEx: Adaptive exponential integrate-and-fire MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Covers the AdEx model, firing patterns, adaptation, and parameter estimation.

Personalized Menu Optimization

Explores Bayesian methods in choice modeling for personalized menu optimization and individual choice prediction.

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Behavior Modeling: Simple Example MOOC: Introduction to Discrete Choice Models

Introduces behavior modeling through a simple example, focusing on analyzing the market for electric cars.

Causal Inference: Estimands and Ontologies

Explores causal inference, emphasizing the importance of committing to an ontology for drawing causal inferences and selecting appropriate estimands.

Learning Chemical Reaction Networks

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.

Debiased Whittle Likelihood: Time Series and Spatial Data

Explores the Debiased Whittle likelihood for time series and spatial data, focusing on fitting spectral density to the periodogram for better predictions and parameter estimation.

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Data Science Essentials

Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.

Intro to Quantum Sensing: Parameter Estimation and Fisher Information

Introduces Fisher Information for parameter estimation based on collected data.

Linear Regression: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.