• Good for environments that are deterministic, observable, static, and completely known.

• A problem consists of five parts: the initial state, a set of actions, a transition model, a goal test function and a path cost function.

• Bread-first search.

• Uniform-cost search.

• Depth-first search.

• Iterative deepening search (depth limit).

• Bidirectional Search.

• Best-first search.

• Greedy best-first search.

• A* search.

• Recursive best-first search.

• Simplified memory bounded A*.

• Defined by an initial state, legal action in each state, the result of each action, a terminal test and a utility function that applies to terminal states.

• Minimax algorithm for two adversaries.

• Alpha-Beta search algorithm.

• Solve Constraint satisfaction problems that represent a state with a set of variable/value pairs and represent the conditions for a solution by a set of contraints on the variables.

• Analysis of consistency: node, arc, path and k-consistency.

• Backtracking search.

• Minimum-remaining-values and degree heuristics.

• Min-conflicts heuristic.

• Cutset conditioning and tree decomposition.

• Knowledge is contained in the form of sentences in a knoledge representation language that are stored in a knowledge base.

• A Logic agent is composed of a knowledge base and an inference mechanism.

• A representation language is defined by its syntax and its semantics.

• It is a simple language consisting of proposition symbols and logic connectives.

• Inference rules.

• Local search methods such as WalkSAT.

• Logical state estimation.

• Decisions via SAT solving.

• The sysntax of first-order logic builds on that of propositional logic. It adds terms to represent objects, and has universal and existential quantifiers.

• Inference rules.

• Unification.

• Generalized Modus Ponens: Foward-chaining and backward-chaining algorithms.

• Demodulation, paramodulation.

• Provide a framework that allows you to numerically encode linguistic expressions and through that gives you a flexible rule-based system.

• Fuzzyfication, Rule evaluation, Aggregation, Defuzzyfication.

• Semantic Networks

• Description logics.

• Nonmonotonic logics.

• Truth maintenance systems.

Supervised Learning

Unsupervised learning

Reinforcement Learning

The model observes some example input-outpu pairs and learns a function that maps from input to output.

The model learns patterns in the input even though no explicit feedback is supplied.

The model learns from a series of reinforcements – rewards or punishments.

• Flowchart-like tree sructure, where each internal node denotes a test on an attribute, each branch represents an outvome of the test, and ecah leaf node hold a class label.

• Good for classification problems.

• ID3, C4.5 and CART algorithms.

• Support Custom attribute Selection Measures: Information gain, gain ratio, Gini index, MDL.

• Support Pruning methods for prepruning and postpruning: Cost complexity, pessimistic pruning.

• They are statistical classifiers.

• They can predict class membership probabilities.

• Naive Bayesian Classification: Assume class-conditional independence.

• The learned model is represented as a set of IF-THEN rules.

• Can be extracted from a Decision Tree.

• Support rule induction using a sequential covering algorithm.

• They implement Rule Quality Measures and Rule Pruning Tecniques.

• An esemble combines a series of k learned models (or base classifiers), with the aim of greating an improved composite classification model.

• Tend to be more accurate than its base classifiers.

• Bagging.

• Boosting and Adaptive Boosting.

• Random Forests.

• Probabilistic graphical models, which unlike naive Bayesian classifiers allow the representation of dependencies among subsets of atributes.

• Specify joint conditional probability distributions.

• Consists of an input layer, one or more hidden layers, and an output layer. Each layer is made up of units.

• The inputs pass through the input layer and are then wighted and fed simultaneously to a second layer of ‘neuronlike’ units known as hidden layer. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples.

• Can be used also for unsupervised learning.

• Feedfoward Neural Network.

• Radial basis function network.

• Kohonen self-organizing network.

• Learning vector quantization.

• Recurrent Neural Network.

• Associative Neural Network.

• Self-organizing feature map

• Dynamic neural networks.

• Neuro-fuzzy networks.

• Spiking neural networks.

• Models for the clasiffication of both linear and nonlinear data.

• They uses a nonlinear mapping to transform the original training data into a higher dimension. Within this new dimension, they search for the linear optimal separating hyperplane.

• Support various kernel functions as Polynomial kernels of degree h, Gaussian radial basis function kernels or Sigmoid kernels.

• Support custom kernel functions.

• Search for a linear relationship between some explanatory variables and a response variable.

• Support classical linear regression for continuous response and logistic linear regression por nominal response.

• Support more general linear regression models as General Linear Regression or Generalised Linear Regression.

• Support both classical and bayesian approach to linear regression parameters estimation.

• Use all the data to make each prediction, rather than trying to summarize the data first with few parameters.

• Nearest neighbors.

• Locally weighted regression.

• Genetic Algorithms.

• Rough Set Approach: Good for noisy data.

• Fuzzy Set Approaches: Based on Fuzzy Logic Rules.

• Active Learning: Iterative type of supervised learning that is suitable for situations where data are abundant, yet the class lasbels are scarce or expensive to obtain. The learning algortihm is active in that it can purposefully query a user for labels.

• Sentiment Classification: The classification task is to automatically label the data as eithe positive or negative.

• These models partition a set of data objects into subsets. Each subset is a cluster, such that objects in a cluster are similar to one another, yet dissimilar to objects in other clusters.

• Partitioning methods: Given a set of n objects construct k partitions of the data, where ecah partition represent a cluster and k <=n.

• Hierarchical methods: Create a hierarchical decomposition of the given set of data objects.

• Density-based methods: Their general ideais to continuegrowing a given cluster as long as the density in the neighborhood exceeds some threshold.

• K-Means: A Centroid-Based Technique. Sensitive to outliers.

• K-Medoids: Less sensitive to outiers. Use absolute-error criterion.

• Agglomerative hierarchical clustering method: Starts by letting each object form its own cluster and iteratively merges clusters into larger and larger cluster.

• Divisive hierarchical clustering method: Starts by placing all the objects in one cluster. It then divides the root cluster into several smaller subclusters.

• The representation commonly used is a dendrogram.

• Algorithms: BIRCH, Chamaleon, Probabilistic Hierarchical Clustering that usesprobabilistic models to measure distances between clusters.

• DBSCAN.

• OPTICS.

• DENCLUE.

• Grid-based method: Itquantizes the object space into a finite number of cells that form a grid structure on which all operations for clustering are perfomed. STING and CLIQUE.

• Probabilistic Model-Based Clusters: Assume that a hidden category is a distributionover the data space, which can be matehmatically represented using a probability density function. Expectation-Maximization Algorithm.

• Biclustering Methods.

• Search for recurring relationships in a given data.

• Discover interesting associations and correlations between itemsets in trasactional and relational databases.

• Association Rules: consist of first finding frequent itemsets, from which strong association rules in the form A=>B are generated. These rules also satisfy a minimum confidence threshold.

• Candidate generation, Pattern growth, Vertical format.

• Direct Utility Estimation: uses the total, observed reward-to-go for a given state as direct evidence for learning its utility.

• Adaptive dynamic programming: learns a model and a reward function from observations and the uses value or policy iteration to obtain utilities or an optimal policy.

• Temporal-difference methods: update utility estimates to match those of successor states.

• Include finding "best available" values of some objective function given a defined domain, including a variety of different types of objective functions and different types of domains.

• Local search methods such as hill climbing and simulated annealing.

• Local search methods apply to problems in continuous spaces: Linear programming and convex optimization.

• Genetic Algorithms.

• Contingent plans for nondeterministic eviroments.

• Online searchfor exploration problems.

• Particle swarm optimization.

• Provides algorithms and tools for the design and simulation of computer vision and video processing systems.

• Feature Detection and Extraction: Enables you to derive a set of feature vectors, also called descriptors, from a set of detected features.

• Registration and Stereo Vision: Estimates the geometric relationships between images or video frames.

• Object Detection, Motion Estimation, and Tracking.

• Audio transcription.

• Probabilistic language models based on n-grams for language identification, spelling correction, genre classification, and named-entity recognition.

• Text classification.

• Speech recognition.

• Syntax analysis.

• Semantic Interpretation.

• Sequence of data points, measured typically at successive points in time spaced at uniform time intervals.

• Time series correlation.

• Forecasting models based on time series.

• Time series classification.

• White Noise.

• Random walks.

• Autoregressive models.

• Fitted models.

• Linear models.

• Generalised least squares.

• Linear models with seasonal variables.

• Harmonic seasonal models.

• Logarithmic transformations.

• Non-linear models.

• Stationary Models: Fitted MA models, ARMA Models.

• Non-Stationary Models: Non-seasonal and seasonal ARIMA models, ARCH models.

• Spectral Analysis.

• Multivariate models.

• State Space Models.

• Asessing how good or how ‘accurate’ your models are.

• Various types of error measure.

• Confusion matrix.

• Misclassification rate.

• Sensitivity and specificity.

• Precision and recall.

• Computational speed.

• Robustness to noisy data.

• Scalibility.

• Interpretability.

• Holdout Method and Random Subsampling

• Cross-Validation

• Bootstrap.

• Model Selection using statistical Tets of Significance.

• Comparing based on Cost-Benefit and ROC Curves.