Sitemap for project Notes

• Gradient Descent
• Index
• Offline Reinforcement Learning
• Model and Inference
• Reinforcement Learning (Aalto University ELEC-E8125 2022)
• Markov Random Fields
• Thought Provoking
• Scientific Communication
• Online Presence
• PC Algorithm
• Computer Architecture
• JAX
• Contextual Bandits
• Conjugate Gradient
• Probabilistic Numerics
• Logging (Python)
• Huffman Coding
• Prefix-free Code
• Kraft Inequality
• Entropy Bound
• Interesting Facts
• Interventions, Where and How? Experimental Design for Causal Models at Scale - 2022
• Bookmarks
• Causal Entropy Optimization - 2023
• Thesis
• Bayesian Data Analysis (Aalto University CS-E5710 2022)
• Gaussian Processes (GPs)
• Bayesian Active Learning for Classification and Preference Learning - 2011
• Pygmalion Effect
• CCS22: Radical Complexity, Radicar Uncertainty Bounded Rationality - Jean-Philippe Bouchau - 2022
• Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design - 2021
• Questions of Essence
• Scratch
• Random Variables
• Programming Languages
• Comprehensive Input
• Web development
• Markov Chain Monte Carlo (MCMC)
• Numpy
• Adaptive Design in Real Time
• Frames (Robotics)
• SE(n)
• Descent Direction Iteration
• Deep Learning (Aalto University CS-E4890 2022)
• Twists
• Exponential coordinates
• Optimal Design of Interventions for Causal Discovery in Genomics
• Bayesian Experimental Design (BED)
• Markov Decision Processes (MDPs)
• Graphoids
• Identifiability
• so(3)
• Line Search
• A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data - 2011
• Prior Knowledge Elicitation: The Past, Present, and Future - 2021
• The Modern Intellectual Tradition
• How Do People Get New Ideas?
• Deep Generative Modelling (Aalto University CS-E407509 2022)
• How to Win at College
• Logistic Regression
• Lebesgue Measure
• org-projectile
• Foundations and Advances in Deep Learning
• Zero-Knowledge Proofs
• Polyphasic Sleep
• Nix
• Talk: Learning to Reconstruct Shapes from Unseen Classes (NeurIPS 2018 Oral) - 2020
• Phase Retrieval Under a Generative Prior: Oral Presentation (NeurIPS 2018) - 2019
• The Wonderful and Terrifying Implications of Computers That Can Learn \textbar Jeremy Howard - 2014
• Understanding Deep Learning Requires Rethinking Generalization - 2017
• Densely Connected Convolutional Networks - 2017
• NIPS: Oral Session 8 - Michael Schober - 2016
• Oral Session: Sampling from Probabilistic Submodular Models - 2016
• NIPS: Oral Session 4 - Ilya Sutskever - 2016
• Ali Rahimi’s Talk at NIPS(NIPS 2017 Test-of-time Award Presentation) - 2017
• RNN Symposium 2016: Jürgen Schmidhuber - Intro to RNNs and Other Machines That Learn Algorithms - 2017
• Wirth's law
• Context-Specific Conditional Independence
• Inverse Reinforcement Learning (IRL)
• A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress - 2021
• Stackelberg Games
• Theory of Mind
• Decentralized Partially Observable Markov Decision Process (Dec-POMDP)
• Do We Need Attention? A Mamba Primer - 2024
• Data Is as Data Does: The Influence of Computation on Inference - 2024
• Associative Scans
• Pop Culture
• Serendipity
• pip
• Efficient Exploration in Bayesian Optimization – Optimism and Beyond by Andreas Krause - 2021
• Compiler Bootstrapping
• Disabling Cookie Popups
• Deep Ensembles
• Design Patterns
• Measure-Theoretic Probability
• Self-Organized Search
• Climate Change
• Habitus
• DNA
• Cold Posteriors
• Frequentist Statistics
• Differential Geometry
• Cooking
• Stoicism
• Typical sets
• Pair Programming
• eglot
• Multi-Fidelity Active Learning with GFlowNets - 2023
• Better Training of GFlowNets with Local Credit and Incomplete Trajectories - 2023
• Survival Instinct in Offline Reinforcement Learning and Implicit Human Bias in Data - 2023
• Flow Network Based Generative Models for Non-Iterative Diverse Candidate Generation - 2021
• NeurIPS Tutorial on Machine Learning for Theorem Proving - 2023
• NeurIPS Tutorial on Machine Learning for Theorem Proving - 2023
• Dungeons and Dragons
• Shrinkage Priors
• Robust Machine Learning
• R Programming
• Graph Laplacian
• NetworkX
• Lagrangian Duality
• Whitening
• Data Augmentation
• Cybersecurity
• Self-Hosted Applications
• DO THIS Every Day To Melt The FAT AWAY BUILD MUSCLE | Sal DiStefano - 2022
• Why You’re Not Losing Fat Building Muscle (Avoid These Mistakes) | Dr. Andy Galpin - 2023
• Visualization
• Imprecise Probabilities
• Human-Computer Interaction (HCI)
• Computational Rationality
• Internships
• User Modelling
• Internet Relay Chat (IRC)
• LangChain
• Large Language Models (LLMs)
• Messaging
• Lean Theorem Prover
• TMDA
• Mathematical Streetsmarts
• Side-Effects (Programming)
• Adversarial Examples in Deep Learning
• Shattered Gradients
• Reichenbach's Common Cause Principle
• T-Distributed Stochastic Neighbour Emeddings (t-SNE)
• Beam Search
• Probability Theory
• Distributed Computing
• Beamer (LaTeX)
• MLOps
• Diffusion Models
• JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models - 2023
• Deep Learning
• Strengthening Nonparametric Bayesian Methods with Structured Kernels - 2022
• Elias Bareinboim (Columbia) – Causal Data Science - 2019
• Bayesian Inference, Shakir Mohamed - MLSS 2020, Tübingen - 2020
• Stephan Mandt @ ICBINB Seminar Series - 2023
• STOC 2021 - 50th Anniversary of the Cook-Levin Theorem - 2021
• Anca Dragan (UC Berkeley): "An Optimization-Centric Theory of Mind for Human-Robot Interaction" - 2019
• John Tsitsiklis (MIT): "The Shades of Reinforcement Learning" - 2019
• Russ Tedrake (MIT): "Learning Manipulation — and Why I (Still) like F=ma" - 2019
• Heal Knee Pain Skyrocket Athleticism - 2021
• Gareth Roberts – Bayesian Fusion - 2020
• Pattern Recognition and Machine Learning (Information Science and Statistics) - 2006
• AI for Science: An Emerging Agenda - 2023
• Property-Based Testing - Lucy Mair - NDC London 2023 - 2023
• Python Design Patterns - 2018
• Bayesian Experimental Design: A Review - 1995
• NP-complete Problems and Physical Reality - 2005
• Model-Based Multi-agent Reinforcement Learning for AI Assistants - 2023
• An Interactive Approach for Bayesian Network Learning Using Domain or Expert Knowledge - 2013
• Common Inaccuracies in Multi-Agent RL Research - 2021
• Can’t Hurt Me - 2020
• Thinking, Fast and Slow - 2011
• Simon Ward-Jones - Introducing More of the Standard Library | PyData London 2022 - 2022
• Active Learning of Causal Bayes Net Structure - 2006
• Reinforcement Learning with Prototypical Representations - 2021
• Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11 - 2018
• Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 - 2019
• Leverage Dopamine to Overcome Procrastination Optimize Effort | Huberman Lab Podcast - 2023
• Isaac Slavitt - The 10 Commandments of Reliable Data Science | PyData Global 2022 - 2023
• Managing Complex Data Science Experiment Configurations with Hydra - Presented by Michal Karzynski - 2022
• Carl Meyer - Type-checked Python in the Real World - PyCon 2018 - 2018
• Ilinca Barsan A Guide to Data Science as a Creative Discipline | PyData NYC 2022 - 2023
• Judith van Stegeren - Practical Code Archaeology - 2023
• Cheuk Ting Ho: I Hate Writing Tests, That’s Why I Use Hypothesis | PyData Tel Aviv 2022 - 2023
• The Flaws of Inheritance - 2022
• How To Build Endurance In Your Brain Body - 2021
• Talk: Language as a Dynamical System - Prof. Dr. Michael Spivey (California, Merced) - 2022
• Language as a Dynamical System - 2001
• AI Driven Design Approach - 2020
• Equilibrium Computation and the Foundations of Deep Learning - 2021
• Mathematics for Machine Learning - 2020
• Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search - 2020
• CLAPEM 2019 I Chris Holmes-Bayesian Nonparametric Learning through Randomized Loss Functions - 2019
• #063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness Causality - 2022
• Effective Pandas I Matt Harrison I PyData Salt Lake City Meetup - 2021
• Marcus Hutter: Foundations of Induction - 2022
• Dr. Alia Crum: Science of Mindsets for Health Performance | Huberman Lab Podcast #56 - 2022
• Marrying Graphical Models Deep Learning - Max Welling - MLSS 2017 - 2017
• Advances in Neural Processes - 2021
• How to Write a Great Research Paper - 2016
• Jeff Cavaliere: Optimize Your Exercise Program with Science-Based Tools | Huberman Lab Podcast #79 - 2022
• How to Control Your Metabolism by Thyroid Growth Hormone - 2021
• How to Lose Fat with Science-Based Tools - 2021
• Is There a Framework for Deep Learning in Multi-Agent Settings? - 2022
• Reinforcement Learning in Recommender Systems: Some Challenges - 2019
• "Statistical Physics of Artificial Neural Networks" by Prof. Lenka Zdeborova - 2021
• Joris Mooij: Joint Causal Inference: A Unifying Perspective on Causal Discovery - 2020
• The Science of Cause and Effect: From Deep Learning to Deep Understanding - 2022
• Using Failures, Movement Balance to Learn Faster - 2021
• HIGGS: The Invention and Discovery of the “God Particle.” - 2013
• Zero-Shot Assistance in Sequential Decision Problems - 2022
• Dr. Wendy Suzuki: Boost Attention Memory with Science-Based Tools | Huberman Lab Podcast #73 - 2022
• Focusing Your Unconscious Mind: Learn Hard Concepts Intuitively (And Forever) - 2022
• Occam’s Razor Is Insufficient to Infer the Preferences of Irrational Agents - 2019
• Cynthia Rudin @ ICBINB Seminar Series - 2022
• Science of Muscle Growth, Increasing Strength Muscular Recovery - 2021
• Causality - 2009
• Machine Teaching of Active Sequential Learners - 2019
• Causal Parrots: Large Language Models May Talk Causality But Are Not Causal - 2023
• Causal Bayesian Optimization - 2020
• Deep Bayesian Active Learning with Image Data - 2017
• Direct Preference Optimization: Your Language Model Is Secretly a Reward Model - 2023
• Pretraining Task Diversity and the Emergence of Non-Bayesian in-Context Learning for Regression - 2023
• Unlock Creative Genius like Da Vinci and Richard Feynman | Tiago Forte - 2023
• Original Father of AI on Dangers!(Prof. Jürgen Schmidhuber) - 2023
• Thierry Bodineau - Nonequilibrium Statistical Mechanics & Large Deviation Theory - 2016
• Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning - 2023
• Sergey Levine on the Bottlenecks to Generalization in RL and Picking Good Research Problems - 2023
• Ben Eysenbach on Designing Simpler and More Principled RL Algorithms - 2023
• Ben Eysenbach Thesis Defense - 2023
• The Large Deviation Approach to Statistical Mechanics - 2009
• Eight Things to Know about Large Language Models - 2023
• The Definition of Numerical Analysis - 1992
• MIA: Tamara Broderick, Edge-exchangeable Graphs, Clustering, and Sparsity - 2017
• Reinforcement Learning: An Introduction - 2018
• Curiosity in Multi-Agent Reinforcement Learning - 2019
• Richard Hennig Jason Gibson - AI-driven Workflows for the Discovery of Novel Superconductors - 2023
• Deep Symbolic Regression: Recovering Mathematical Expressions from Data via Risk-Seeking Policy Gradients - 2021
• On the Fine Structure of Large Search Spaces - 1999
• #038 - Prof. KENNETH STANLEY - Why Greatness Cannot Be Planned - 2021
• Human-in-the-Loop Assisted de Novo Molecular Design - 2022
• How to Use the Placebo Effect to (Actually) Feel Better - 2023
• Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference - 2017
• Expert Knowledge Elicitation: Subjective but Scientific - 2019
• Active Invariant Causal Prediction: Experiment Selection through Stability - 2020
• Targeted Active Learning for Bayesian Decision-Making - 2021
• Bayes-Adaptive POMDPs - 2007
• BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design - 2020
• Empirically-Based Modeling and Mapping to Consider the Co-Occurrence of Ecological Receptors and Stressors - 2018
• A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships - 1997
• Inverse Decision Modeling: Learning Interpretable Representations of Behavior - 2021
• Lost Relatives of the Gumbel Trick - 2017
• K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation - 2006
• Google
• Interoperability
• Firefox
• hydra-zen (Python)
• zk (emacs)
• surf
• CSC Supercomputers
• Configuration
• Change of variables (Probability)
• Pain as Perception
• Transformers
• In-Context Learning (ICL)
• Decorator Pattern
• Bridge Pattern
• Adapter Pattern
• Credit Assignment
• Free Energy
• Goal-Conditional Reinforcement Learning
• Contrastive Learning
• Self-Supervised Learning
• Rashomon Set
• Rashomon Effect
• Rashomon Capacity
• Predictive Multiplicity
• Prompt Engineering
• Existence Theorems
• Singular Value Decomposition (SVD)
• Aldous-Hoover Representation Theorem
• Proportion-Integral-Derivation (PID) Control
• Kalman Filter
• Newton's method
• Discrete Optimization
• Truncated Rollout
• Planning
• Baber-Agakov Bound
• Training, validationa and test sets
• Statistical Intervals
• Multi-Agent Deep Deterministic Policy Gradient (MADDPG)
• Independent Q-Learning
• Partially Observable Stochastic Game
• Exploration-Exploitation Tradeoff
• Researchers
• Thought Experiments
• Density Functional Theory
• Risk-Seeking Policy Gradients
• Genetic Algorithms
• Machine Learning
• Distribution Shift
• Markov Game/Stochastic Game
• Psychology
• Posterior Bootstrap
• Bayesian Bootstrap
• Proof Number Search
• Retrosynthetic Planning
• Retrosynthesis
• A* search
• Generalized Hidden Parameter Markov Decision Process (GHP-MDP)
• Formal Grammar
• Molecule Generation
• Collapsed Gibbs Sampler
• Domain Specific Language (DSL)
• Human-In-The-Loop (HITL) Machine Learning
• Graph Neural Network (GNN)
• Symbolic Regression
• Wignerian Prior
• There and Back Again - A Tale of Slopes and Expectations
• The Automated Statistician
• Causal Representation Learning
• Causal Discovery
• Causal Transportability
• Causal Inference
• Model Predictive Control (MPC)
• AI for Science
• Property-Based Testing
• Advanced Real Analysis I (KTH SF2743)
• Dynamic Programming
• Bayes-adaptive MDP (BAMDP)
• Denoising
• Multi-Modal Learning
• Learned Helplessness
• Monte Carlo Fusion
• Bayesian Fusion
• Software Testing
• Huberman #72 - Understand & Improve Memory Using Science-Based Tools
• Huberman - Dr. Elissa Epel: Controll Stress for Healthy Eating, Metabolism & Aging
• Huberman #10 - Tools for Managing Stress & Anxiety
• Huberman #82 - The Science & Treatment of Bipolar Disorder
• Machine Learning Advanced Probabilistic Methods (Aalto University CS-E4820 2023)
• Emacs Demo
• Anki
• Code Smells
• Sailing Navigation 1
• Maximum A Posteriori
• Poetry
• Variational Inference: Foundations and Modern Methods (NIPS 2016 Tutorial)
• Deep Reinforcement Learning (UC Berkeley CS-285 2020)
• Emacs
• yasnippet
• tempel
• sioyek
• Building from Source
• The Turing Way
• Neurons
• Gaussian Processes (Aalto University CS-E4895 2023)
• Gaussian Distribution
• Approximate Bayesian Inference
• Laplace Approximation
• Defensive Programming
• Bulwark (Python)
• Django (Python)
• Audible
• Syncthing
• Hydra (Python)
• Tractable Uncertainty for Causal Structure Learning @ APML Seminar Series
• Ubuntu
• Flow
• Flow Programming
• Black (Python)
• DL1 Deep Learning (University of Amsterdam)
• Generative Adversarial Networks (GANs)
• Causality Discussion Group - Abstracting Causal Models
• rpy2
• Loopy Belief Propagation
• Variation Inference (VI)
• Expectation Propagation (EP)
• Probability of Necessity and Sufficiency (PNS)
• Causality Discussion Group - Representation Learning: A Causal Perspective
• Numerics of Machine Learning 2023 (University of Tuebingen)
• Jorma Rissanen's Festschrift
• Algorithmic Probability Theory
• Kolmogorov Complexity
• Cournot's principle
• No Free Lunch (NFL) Theorems
• Minimum Description Length (MDL)
• SF2971 - Martingales and Stochastic Integrals (KTH Stockholm)
• Dependency Injection
• Complex Systems
• Cardio
• Resistance Training
• Bayesian Statistics
• De Finetti's Theorem
• Meditation
• Digital Garden
• Causal Multi-Armed Bandits
• Causal Identification
• Do-calculus
• Annealed Importance Sampling (AIS)
• Importance-Weighted Autoencoder (IWAE)
• Bayesian Quadrature
• Sequential Experimental Design (SED)
• Brain Derived Neurotrophic Factor (BDNF)
• Cortex
• Hippocampus
• Variational Autoencoder (VAE)
• Attention Mechanism
• Docker
• Information Geometry
• Machine Learning Community
• Differential Privacy
• Jupyter notebooks
• Lambda Calculus
• Simulation-based Inference
• Lottery Ticket Hypothesis
• Mastodon
• Game of Life
• Matplotlib
• ImageNet
• Datasets
• DAGGER Algorithm
• Trajectory Optimization
• Model-Based Methods in Reinforcement Learning
• Back-Propagation-Through-Time (BPTT)
• Dyna
• Continual Learning
• Conditional Neural Process (CNP)
• Neural Process
• Batch Normalization
• Adam
• RMSProp
• Adagrad
• Empirical Risk Minimization (ERM)
• Deep Neural Networks
• Optimization in Deep Learning
• NeurIPS 2022
• Falsifiability
• Pandas
• Time Management
• Continual Learning (University of Pisa, Continual AI, AIDA)
• Probability of Necessity (PN)
• Efficiency of Representations
• Probability of Sufficiency (PS)
• Non-spuriousness of Representations
• Monte-Carlo Policy Iteration
• Bellman's Principle of Optimality
• Watanabe-Akaike Information Criterion (WAIC)
• Parento Smoothed Importance Sampling (PSIS)
• Bayesian Optimization (BO)
• Toy Problems
• Workflows
• Vector Space
• Dimension
• Matrix Rank
• Matrix Multiplications
• Linear maps
• Number Field
• Matrices
• What is a Doctoral Thesis
• Functional Programming
• Pure Functions
• Celluloid
• Daft
• einops
• Vectorization
• Bayesian Neural Networks (BNNs)
• Categories for AI
• Total Causal Effect
• org-ref
• Autocorrelation
• Potential Scale Reduction Factor (PSRF)
• Law of Large Numbers (LLN)
• Pareto-k diagnostic
• Effective Sample Size (ESS)
• Sampling Importance Resampling (SIR)
• Central Limit Theorem (CLT)
• Box-Muller Method
• Sufficient Statistic
• Vim
• Podcasts
• mu4e
• RSS
• Tidy Data
• TRAMP
• org-roam
• Free and Open Source Software (FOSS)
• MLflow
• Sacred (Python)
• Control Theory
• pass
• conda
• Completed Partially Directed Acyclic Graph (CPDAG)
• Borel σ-algebra
• Probability Space
• Measure Space
• Measure
• Measurable Space
• Set Rings and Algebras
• σ-Algebra
• Total Derivative
• Habit Formation
• Nervous System
• Philosophical Suicide
• Calisthenics
• Principle of Triviality
• Hessian
• Backpropagation
• Philosophy
• Finnish
• Rademacher Complexity
• VC Dimension
• Shannon's Perfect and Imperfect Secrecy Theorems
• Signed Measure
• Set Fields
• Cesàro Means
• Entropy Rate
• Total Variation
• Mathematical Inequalities
• Kullback-Leibler (KL) Divergence
• Mutual Information
• Shannon Entropy
• Regular Expressions
• Policy Improvement Theorem
• Upper Confidence Bound (UCB)
• Softmax Exploration
• Notation
• Heavy-Tailed Distributions
• Submodular Optimization
• Bayesian Nonparametrics
• Dirichlet Processes
• Meta-Learning
• Factor Graphs
• Wavelet Transforms
• Coderefinery Scientific Computing Workshop Summer 2022
• LaTeX
• Zotero
• Extensive Form Games
• Counterfactual Regret Minimization
• No-Regret Learning in Extensive Form Games
• Bayesian Reinforcement Learning
• Invariant Causal Prediction
• Structural Causal Models (SCMs)
• Front-door Criterion
• Markov Blanket
• Back-door Criterion
• Equilibrium Computation, GANs, and the Foundations of Deep Learning
• Coderefinery Workshop Spring 2022
• Sensitivity Analysis
• Boltzmann Distribution
• Leapfrog Integrator
• Symplectic Integrator
• Ordinary Differential Equations (ODEs)
• Hamiltonian Dynamics
• Phase Space
• Hamiltonian Monte Carlo (HMC)
• Bayesian Modelling
• Markov Chain
• Concentration Inequalities
• Markov Equivalence Classes (MECs)
• Graph
• d-separation
• Actor-Critic Methods
• Gibbs Sampler
• Importance Sampling
• Matrix Calculus
• Inverse Transform Sampler
• Rejection Sampling
• Metropolis-Hastings Algorithm
• Control Variates
• Jacobian
• Divergence
• Vector Field
• Scalar Field
• Einstein Summation Convention
• Curve
• Brent-Dekker method
• Multilater Perceptrons (MLPs)
• Torque/Moment
• Wrench vector
• REINFORCE Algorithm
• Variance Reduction
• Policy Gradients
• org-reveal
• Conditional Independence
• Pointers
• C++
• Directed Acyclic Graph (DAG)
• GNU Make
• isync
• Shell scripting
• Research tips
• Courses
• Index
• Robotic Manipulation (Aalto University ELEC-E8126 2022)
• Git
• Research Software Engineering
• Vim grammar
• Build system
• SSH
• Multi-Armed Bandits (MABs)
• Policy Iteration
• Recipes
• Temporal-Difference (TD) learning
• Monte-Carlo policy evaluation
• Reinforcement learning (RL)
• Python
• ε-greedy exploration
• Value Iteration
• Bellman Equations
• Reading
• Experimental Designs
• Co-integration
• Good habits
• Programming
• CLIP
• Communicative Rationality
• Partially Observable Markov Decision Processes (POMDPs)
• Quotes
• Machine Teaching
• Health and Fitness
• Teaching Resources
• Literature Reviews
• Bias-Variance Tradeoff
• Multi-Agent Reinforcement Learning (MARL)
• Research
• Jakob Foerster - Zero-Shot (Human-AI) Coordination (in Hanabi) and Ridge Rider - 2021
• A Game-Theoretic Approach to Offline Reinforcement Learning - 2022
• Inverse Game Theory for Stackelberg Games: The Blessing of Bounded Rationality - 2022
• Generative Flow Networks (GFlowNets)
• Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback - 2023
• Topics in Reinforcement Learning (Arizona State University CSE691)
• UAI 2023 Tutorial: Causal Representation Learning - 2023
• The Indian Chefs Process - 2020
• PyTorch
• Federated Learning (Aalto University CS-E4740 2024)
• Maps
• direnv
• PostgreSQL
• Hidden Markov Models (HMMs)
• Randomized Controlled Trials (RCTs)
• Note Taking
• Linux
• Markov Conditions
• Slice Sampler
• Simulated Annealing
• Gradient
• Directional Derivative
• TODO
• Randomized Response
• Eigen (C++)
• Infinite exchangeability
• Robot Operation System (ROS)
• SO(n)
• Configuration space (Robotics)
• Holonomic constraints
• Degrees of freedom (Rigid bodies)
• Topological spaces
• Robotic joints
• Eligibility traces
• Deep Q-learning
• Q-learning
• Policy evaluation
• SARSA
• Iterative policy evaluation
• Tail call optimization
• LLVM
• Just-In-Time (JIT) Compilation
• Lottery Paradox
• Stochastic Gradient Descent
• Newton's laws
• Gauge theory
• Noether's theorem
• Neurosymbolic Programming
• Causality
• org-mode
• Principal Component Analysis (PCA)
• Availability Bias
• Drift Analysis
• Study Skills
• Competitive Programming
• Bayesian Networks (BNs)
• Runtime and Compile Time Errors
• Bayesian Decision Theory
• Common Sense
• Rao-Blackwellization
• Automatic Differentiation (Autodiff)
• Bounded Rationality
• The Book of Why: The New Science of Cause and Effect - 2018
• Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks - 2022
• Andrew Howes - Computational Rationality - 2020
• Photographic Image Priors in the Era of Machine Learning - 2023
• Can Humans Be out of the Loop? - 2022
• Science-Based Mental Training Visualization for Improved Learning | Huberman Lab Podcast - 2023
• Jocko Willink: How to Become Resilient, Forge Your Identity Lead Others | Huberman Lab Podcast 104 - 2022
• Rashomon Capacity: A Metric for Predictive Multiplicity in Classification - 2022
• Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents’ Capabilities and Limitations - 2020
• A Review of Modern Computational Algorithms for Bayesian Optimal Design - 2016
• Improving Mutual Information Estimation with Annealed and Energy-Based Bounds - 2022
• rsync
• iGraph (Python)
• Stress
• Reinforcement Learning From Human Feedback (RLHF)
• Slurm
• Game theory
• Gaussian Process Latent Variable Models (GPLVMs)
• Nutrition
• Data-Efficient Graph Grammar Learning for Molecular Generation - 2023
• Relational Models Theory
• Information Fusion
• Monte-Carlo Tree Search (MCTS)