Expected workload ⌚ 35-45 mins

• Essential definitions and the shipping service concept
• Building blocks of models:
  • Variables, parameters, inputs, outputs
  • Relationships (governing equations)
• Interactive examples:
  • HR_max = 220 - Age (linear model)
  • Muscle force-length relationship (quadratic model)
  • Hill muscle model and power-velocity relationship
  • Professional cycling performance estimation
• Linear vs nonlinear models
• Model calibration and parameter fitting
• The modeling process: observation → conceptualization → formulation → identification → validation

Expected workload ⌚ 45-60 mins

• Parameter estimation and optimization
• Root Mean Squared Error (RMSE) and fitting process
• Interactive optimization examples
• HR_max = 220 − Age model analysis:
  • Historical origins and limitations
  • Alternative models (Tanaka, Gulati)
  • Population-specific calibration
• Interpolation vs extrapolation:
  • Olympic records and linear projections
  • Physical limitations and model boundaries
  • Dangers of extrapolation beyond observed data
• Overfitting: the hidden cost of complexity
  • Chicken growth polynomial example
  • Bias-variance tradeoff
  • Model selection guidelines

Expected workload ⌚ 40-50 mins

• VO₂ Kinetics (static version):
  • One-exponential model
  • Amplitude, time constant, and baseline parameters
  • Limitations and extensions
• Lactate Kinetics (static version):
  • Two-pool lactate model
  • Biphasic decay and clearance phases
  • Post-exercise lactate dynamics
• Supercompensation Model:
  • Banister fitness-fatigue model
  • Training stimulus and adaptation curves
  • Performance = fitness - fatigue
• Intensity-Duration Relationships:
  • Critical Power (CP) and Wp models
  • 3-min all-out test implementation
  • Anaerobic Power Reserve (APR) model
  • 3-Parameter CP and Omni-Domain models

Expected workload ⌚ 50-65 mins

• Introduction to differential equations
• Static vs dynamic models
• Time derivatives and system evolution
• First-order dynamic systems:
  • VO₂ Kinetics (dynamic version)
  • Time constants and exponential responses
  • Finite difference implementation
• Second-order dynamic systems:
  • Newton second law foundations
  • Mass-spring-damper systems
  • Mountain bike suspension model
  • Natural frequency and resonance
• Locomotor sports equations:
  • Cycling dynamics and power equations
  • Aerodynamic drag, rolling resistance, gravity
  • Force-to-power conversion
  • Numerical integration methods

Expected workload ⌚ 40-50 mins

• Neural network fundamentals:
  • Perceptrons: weights and biases
  • Connection to linear regression
  • Interactive 4-parameter neural network
• Training vs validation:
  • Parameter optimization process
  • Mean Squared Error (MSE) minimization
  • Manual training challenges
• The Curse of Dimensionality:
  • Parameter explosion in deep networks
  • GPT models: 175B to 1.8T parameters
  • Overfitting, computational costs, data requirements
  • Interpretability challenges
• How does an LLM work?:
  • Text prediction as a modeling service
  • Connecting to familiar sports science models
  • Statistical pattern recognition at scale
  • Input-parameter-output framework applied to language

Expected workload ⌚ 40-50 mins

• Real-world decision making:
  • Car crash data and insurance premium models
  • Vehicle weight vs safety trade-offs
  • Models affecting policy, pricing, and regulation
  • Ethical responsibilities of modelers
• Association vs Causation:
  • Ice cream sales vs shark attacks example
  • Spurious correlations and confounding variables
  • Experimental design and causal inference
  • Training load and injury relationships
• Universal mathematical language:
  • First-order differential equation across disciplines
  • VO₂ kinetics, moving averages, Banister model, RC circuits
  • Cross-disciplinary modeling techniques
  • Mathematical analogies and system universality