Sitemap for project notes-org
- (draft) Conjugate Gradient
- A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models
- A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data
- A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships
- Active Structure Learning of Bayesian Networks in an Observational Setting
- Actor-Critic Methods
- Bayesian Modelling
- Bayesian Network
- Bayesian Optimal Experimental Design (BOED)
- Bellman Equations
- Bias-Variance tradeoff
- Boltzmann Distribution
- Book of Why
- Bookmarks
- Brent-Dekker method
- Build system
- C++
- CS-E407509 - Deep Generative Modelling
- CS-E4890 - Deep Learning
- Can Humans Be out of the Loop?
- Coderefinery Workshop Spring 2022
- Cointegration
- Communicative Rationality
- Complexity: A Guided Tour
- Comprehensive Input
- Concentration Inequalities
- Conditional Independence
- Configuration space (Robotics)
- Contextual Bandits
- Control Variates
- Courses
- Curve
- Deep Q-learning
- Degrees of freedom (Rigid bodies)
- Descent Direction Iteration
- Directed Acyclic Graph (DAG)
- Directional Derivative
- Divergence
- ELEC-E8125 Reinforcement Learning
- ELEC-E8126 Robotic Manipulation
- Eigen (C++)
- Einstein Summation Convention
- Eligibility traces
- Experimental Designs
- Expert Knowledge Elicitation: Subjective but Scientific
- Exponential coordinates
- FCAI MLCS - Computational Rationality
- Frames (Robotics)
- GNU Make
- Game theory
- Gauge theory
- Gibbs Sampler
- Git
- Good habits
- Gradient
- Gradient Descent
- Graph
- Hamiltonian Dynamics
- Hamiltonian Monte Carlo (HMC)
- Hidden Markov Model (HMM)
- Holonomic constraints
- How Do People Get New Ideas? (UNFILED)
- How to Win at College (UNFILED)
- Huberman #7 - Using Failures, Movement & Balance to Learn Faster (UNFILED)
- Importance Sampling
- Index
- Index
- Infinite exchangeability
- Information Fusion
- Interestingness elements for explainable reinforcement learning: Understanding agents’ capabilities and limitations
- Inverse Decision Modeling:Learning Interpretable Representations of Behavior
- Inverse Transform Sampler
- Iterative policy evaluation
- Jacobian
- Just-In-Time (JIT) Compilation
- LLVM
- Leapfrog Integrator
- Line Search
- Lottery Paradox
- Machine Teaching
- Machine Teaching of Active Sequential Learners (TODO)
- Markov Chain
- Markov Chain Monte Carlo (MCMC)
- Markov Conditions
- Markov Decision Processes (MDPs)
- Markov Equivalence Classes (MECs)
- Matrix Calculus
- Metropolis-Hastings Algorithm
- Monte-Carlo policy evaluation
- Multi-Armed Bandits (MABs)
- Multilater Perceptrons (MLPs)
- Newton’s laws
- Noether’s theorem
- Note taking
- Numpy
- Ordinary Differential Equation (ODE)
- PC Algorithm
- Partially Observable Markov Decision Processes (POMDPs)
- Phase Space
- Podcasts
- Pointers
- Policy Gradients
- Policy evaluation
- Policy iteration
- Prior knowledge elicitation: The past, present and future
- Programming
- Programming Languages
- Python
- Q-learning
- Questions of Essence
- Quotes
- REINFORCE Algorithm
- Random Variables
- Randomized Controlled Trial (RCT)
- Randomized Response
- Reading
- Recipes
- Reinforcement Learning with Prototypical Representations
- Reinforcement learning (RL)
- Rejection Sampling
- Relational Models Theory
- Research
- Research Software Engineering
- Research tips
- Robot Operation System (ROS)
- Robotic joints
- SARSA
- SE(n)
- SO(n)
- SSH
- Scalar Field
- Scientific communication
- Sensitivity Analysis
- Shell scripting
- Simulated Annealing
- Slice Sampler
- Stochastic Gradient Descent
- Symplectic Integrator
- TODO
- Tail call optimization
- Temporal-Difference (TD) learning
- The Invention and Discovery of the “God Particle”
- Topological spaces
- Torque/Moment
- Twists
- Value iteration
- Variance Reduction
- Vector Field
- Vim grammar
- Web development
- Wrench vector
- d-separation
- notes
- org-reveal
- so(3)
- ε-greedy exploration