Lectures related to Waterfall model

Catastrophe Modeling: Storms

By Professor David Bresch explores the origins and applications of natural catastrophe modeling, focusing on storms and their impact on risk mitigation and decision-making processes.

Software Development Process

Explores Scrum, the waterfall model, teamwork, code reviews, and asynchronous software design in software development.

Developing a Model: Simple Example

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

Project Management & Innovation: Industrial Insights

Covers industrial project management, scope and time management, risk management, and product development basics.

Developing a Model

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

Additive Manufacturing: Space Mechanisms

Explores additive manufacturing for space mechanisms, covering 3D printing technologies, materials, design rules, and applications in space.

Designing Calculi: Strong Normalization and Error Handling

Covers strong normalization, error handling, and typing rules for Calculi design.

Software Development Processes: Agile Methods

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

Traffic flow modeling: Developing and testing traffic models

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

Transmission Lines: Incremental Model

Covers the incremental model for transmission lines, including Kirchhoff's laws and VSWR calculations.

Software Development: Life Cycle & Methodologies

Explores the software development life cycle, methodologies, tech choices, agile practices, and common tech industry terms.

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.

Machine Learning Biases

Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.

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.

Developing Employees: Team Projects

Emphasizes developing employees through team projects and explores leadership, management, corporate culture, and the 70/20/10 development model.

Europe: Geopolitical Evolution

Delves into Europe's geopolitical evolution, empires, urban networks, and economic challenges.

Urban Infrastructure: Planning and Development

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

Linear Regression: Causality and Model Interpretation

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