Lecture

Developing a Model: Simple Example

Related lectures (24)

Covers the development of a model to identify stable causal relationships between variables.

Explores the shift from software testing to formal specifications, highlighting the significance of clear requirements in software engineering.

From Tests to Proof

Explores software engineering concepts, emphasizing the significance of tests and clear specifications in ensuring software correctness.

Outlier Analysis

Discusses predicting future behavior and analyzing outliers in choice models.

Property-Based Testing: QuickChick

Explores property-based testing using QuickChick, focusing on software correctness, specifications, and dynamic information flow control.

Error Management in LabVIEW

Emphasizes error management in LabVIEW, source file analysis, and program execution with Xcode.

Software Development Processes: Agile Methods

Explores software development processes, comparing Waterfall model with Agile methods, focusing on Scrum, values, principles, and team roles.

IT Solutions for Business Alignment

Covers solutions for aligning business and IT through information modeling, RFP/RFT, and low-code platforms.

Traffic flow modeling: Developing and testing traffic models

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

Linear Regression: Causality and Model Interpretation

Explores linear regression, causality, and model interpretation through geometric analysis and empirical modeling.

Software Correctness: Testing and Verification

Explores software correctness through testing, verification, property-based fuzzing, and dynamic information flow control.

What is Formal Verification?

Introduces formal verification and its advantages over traditional testing methods, discussing automated theorem proving and compiling correctness statements into verification conditions.

Data Integration & Cleaning: Expert Matching & Entity Recognition

Covers data integration, expert matching, entity recognition, performance, scalability, stream data management, tensor decomposition, and concept drift detection.

Discrete choice and machine learning: two complementary methodologies

Explores discrete choice and machine learning as complementary methodologies, discussing supervised learning, model advantages, pitfalls, aggregation bias, probabilistic classification, and panel data.

Data Validation Model: Building and Running

Covers the ValiModeller user's guide, focusing on building and running data validation models.

Prediction Tests

Discusses predicting future behavior, identifying outliers, and improving model specification.

Business-IT Alignment: Requirements Management & RPA

Covers business/IT alignment, requirements management, and an introduction to Robotic Process Automation.

Topographic Mapping & GLM I

Introduces topographic brain mapping, auditory pathways, motor cortex organization, and the General Linear Model for fMRI data analysis.

Urban Infrastructure: Planning and Development

Explores urban infrastructure planning, development models, and challenges of urban growth.

CleanCloud Project Overview: Transitioning to a Post-Fossil Era

Outlines the CleanCloud project, focusing on aerosol-cloud interactions and climate transition to a post-fossil regime.