🤖 Artificial Intelligence 2 Notes
Contents
- 01 Uncertainty
- 02 Bayesian Estimation
- 03 Maximum A Posteriori Estimation
- 04 Maximum Likelihood estimation - Frequentist Framework
- 05 Bernoulli Distribution
- 06 Binomial Distribution
- 07 Poisson Distribution
- 08 PDF vs PMF
- 09 Urn Problem
- 10 Likelihood function
- 11 Maximum Likelihood Estimate and Estimator
- 12 The First-Order Optimality Condition
- 13 Gradient Descent
- 14 Logistic Regression
- 15 Maximum Likelihood Estimation of Logistic Regression
- 16 Cost Function for Logistic Regression
- 17 Main Assumptions in Logistic Regression
- 18 Bayes rule as a classifier
- 19 The Naive Bayes Classifier
- 20 Information Theory
- 21 Self Information
- 22 Entropy
- 23 Joint Entropy and Conditional Entropy
- 24 Relative Entropy (Kullback-Leibler Divergence)
- 25 Cross Entropy
- 26 Mutual Information
- 27 Decision Tree
- 28 Gini Index
- 29 Model Definition
- 30 Preparation for Test Set Bounds
- 31 Test Set Bound (Theorem)
- 32 Training Set Bounds
- 33 Occam’s Razor Bound
- 34 Estimator
- 35 Bias, Variance and Noise
- 36 Bias-Variance Trade-off
- 37 Overfitting vs Underfitting
- 38 Regularisation
- 39 Bootstrapping ensembles
- 40 Probabilistic Graphical Model
- 41 Bayesian Networks
- 42 Formal Meaning of a Bayesian Network
- 43 Probabilistic Relationships (Standard Structures)
- 44 Bayesian Network Inference
- 45 Marginalisation
- 46 State Space Graph and Search Tree
- 47 Generic tree search
- 48 Constraint Satisfaction Problem
- 49 Minesweeper as a CSP
- 50 Variety of CSPs
- 51 Solving CSPs by standard search formulation
- 52 Tree Search vs Local Search
- 53 Local Search for CSPs
- 54 Optimisation
- 55 Game is a search problem
- 56 Minimax
- 57 Game Tree Pruning