Course Information Course Materials AI Resources Quick Links <\/p>\n
Principles of Artificial Intelligence: Study Guide <\/p>\n
Modeling dependence between attributes. The decision tree classifier. Introduction to information theory. Information, entropy, mutual information, and related concepts (Kullback-Liebler divergence). <\/p>\n
Algorithm for learning decision tree classifiers from data. The relationship between MAP hypothesis learning, minimum description length principle (Occam's razor) and the role of priors. <\/p>\n
Ovrfitting and methods to avoid overfitting -- dealing with small sample sizes; prepruning and post-pruning. Pitfalls of entropy as a splitting criterion for multi-valued splits. Alternative splitting strategies -- two-way versus multi-way splits; Alternative split criteria: Gini impurity, Entropy, etc. Cost-sensitive decision tree induction -- incorporating attribute measurement costs and misclassification costs into decision tree induction. <\/p>\n
Dealing with categorical, numeric, and ordinal attributes. Dealing with missing attribute values during tree induction and instance classification. <\/p>\n
Evaluation of classifiers. Accuracy, Precision, Recall, Correlation Coefficient, ROC curves. <\/p>\n
Required Readings <\/p>\n
Recommended Readings <\/p>\n
Introduction to Artificial Neural Networks and Linear Discriminant Functions. Threshold logic unit (perceptron) and the associated hypothesis space. Connection with Logic and Geometry. Weight space and pattern space representations of perceptrons. Linear separability and related concepts. Perceptron Learning algorithm and its variants. Convergence properties of perceptron algorithm. Winner-Take-All Networks. <\/p>\n
Bayesian Recipe for function approximation and Least Mean Squared (LMS) Error Criterion. Introduction to neural networks as trainable function approximators. Function approximation from examples. Minimization of Error Functions. Derivation of a Learning Rule for Minimizing Mean Squared Error Function for a Simple Linear Neuron. Momentum modification for speeding up learning. Introduction to neural networks for nonlinear function approximation. Nonlinear function approximation using multi-layer neural networks. Universal function approximation theorem. Derivation of the generalized delta rule (GDR) (the backpropagation learning algorithm). <\/p>\n
Generalized delta rule (backpropagation algorithm) in practice - avoiding overfitting, choosing neuron activation functions, choosing learning rate, choosing initial weights, speeding up learning, improving generalization, circumventing local minima, using domain-specific constraints (e.g., translation invariance in visual pattern recognition), exploiting hints, using neural networks for function approximation and pattern classification. Relationship between neural networks and Bayesian pattern classification. Variations -- Radial basis function networks. Learning non linear functions by searching the space of network topologies as well as weights. <\/p>\n
Lazy Learning Algorithms. Instance based Learning, K-nearest neighbor classifiers, distance functions, locally weighted regression. Relative advantages and disadvantages of lazy learning and eager learning. <\/p>\n
Additional Information <\/p>\n
The material to be covered each week and the assigned readings (along with online lecture notes, if available) are included on this page. The study guide (including slides, notes, readings) will be updated each week. The assigned readings are divided into required and recommended readings and notes from recitations (if available). You will be responsible for the material covered in the lectures and the assigned required readings. You are strongly encouraged to explore the recommended readings. <\/p>\n
Overview of the course; Overview of artificial intelligence: What is intelligence? What is artificial intelligence (AI)? History of AI; Working hypothesis of AI. Introduction to intelligent agents. Intelligent agents defined. Taxonomy of agents. Simple reflex agents (memoryless agents); agents with limited memory; rational agents; agents with goals; utility-driven Agents. <\/p>\n
You may skip most of these readings if you have prior programming experience in Java. <\/p>\n
Goal-Based Agents. Problem-solving as state space search. Formulation of state-space search problems. Representing states and actions. Basic search algorithms and their properties: completeness, optimality, space and time complexity. Breadth-first search, depth-first search, backtracking search, depth-limited and interative deepening search. <\/p>\n
Heuristic search. Finding optimal solutions. Best first search. A* Search: Adding Heuristics to Branch and Bound Search. Completeness, Admissibility, and Optimality of the A* algorithm. Design of admissible heuristic functions. Comparison of heuristic functions (\"informedness\" of heuristics). <\/p>\n
Problem Solving through Problem Reduction. Searching AND-OR graphs. A*-like admissible algorithm for searching AND-OR graphs. <\/p>\n
Problem solving as Constraint Satisfaction. Properties of constraint satisfaction problems. Examples of constraint satisfaction problems. Iterative instantiation method for solving CSPs. Scene interpretation as constraint propagation (Waltz's line labeling algorithm). Node consistency, arc consistency, and related algorithms. <\/p>\n
Stochastic search: Metropolis Algorithm, Simulated Annealing, Genetic Algorithms. <\/p>\n
Introduction to Knowledge Representation. Logical Agents with explicit knowledge representation. Knowledge representation using propositional logic; Review of Propositional Logic: Propositional logic as a knowledge representation language: Syntax and Semantics; Possible worlds interpretation; Models and Logical notions of Truth and Falsehood; Logical Entailment; Inference rules; Modus ponens; Soundness and Completeness properties of inference. Modus Ponens is a sound inference rule for Propositional logic, but is not complete. Extending modus ponens - the resolution principle. <\/p>\n
Logical Agents without explicit representation. Comparison of logical agents with and without explicit representations. <\/p>\n
FOPL (First-Order Predicate Logic). Ontological and epistemological commitments and Syntax and semantics of FOPL. Examples. Theorem-proving in FOL. Unification, instantiation, and entailment. <\/p>\n
Transformation of FOPL sentences in Clause Normal Form. Resolution by refutation for First Order Predicate Logic. Examples. Automated Theorem Proving. Search Control Strategies for Theorem Proving. Unit Preference, Set of Support and related approaches. Soundness and Completeness of Proof Procedures. Semidecidability of FOPL and its implications. Brief discussion of Datalog (for deductive databases) and Prolog (for logic programming). <\/p>\n
Emerging Applications of Knowledge Representation.. Semantics-Driven Applications. Ontologies. Information Integration. Service Oriented Computing. Semantic Web. Brief overview of Ontology Languages: RDF, OWL. Description Logics - Syntax, Semantics, and Inference. <\/p>\n
Representing and Reasoning Under Uncertainty. Review of elements of probability. Probability spaces. Bayesian (subjective) view of probability. Probabilities as measures of belief conditioned on the agent's knowledge. Axioms of probability. Conditional probability. Bayes theorem. Random Variables. Independence. Probability Theory as a generalization of propositional logic. Syntax and Semantics of a Knowledge Representation based on probability theory. Sound inference procedure for probabilistic reasoning. <\/p>\n
Independence and Conditional Independence. Exploiting independence relations for compact representation of probability distributions. Introduction to Bayesian Networks. Semantics of Bayesian Networks. D-separation. D-separation examples. Answering Independence Queries Using D-Separation tests. <\/p>\n
Probabilistic Inference Using Bayesian Networks. Exact Inference Algorithms - Variable Elimination Algorithm; Message Passing Algorithm; Junction Tree Algorithm. Complexity of Exact Bayesian Network Inference. Approximate inference using stochastic simulation (sampling, rejection sampling, and liklihood weighted sampling <\/p>\n
Making Simple Decisions under uncertainty, Elements of utility theory, Constraints on rational preferences, Utility functions, Utility elicitation, Multi-attribute utility functions, utility independence, decision networks, value of information <\/p>\n
Mid term examination <\/p>\n
Sequential Decision Problems. Markov Decision Processes. Value Iteration. Policy Iteration. Partially Observable MDPs. <\/p>\n
Markov Decision Processes and Sequential Decision Problem. <\/p>\n
Reinforcement Learning. Agents that learn by exploring and interacting with environments. Examples of reinforcement learning scenario. Markov decision processes. Types of environments (e.g., deterministic versus stochastic state transition functions and reward functions, stationary versus non-stationary environments, etc.). <\/p>\n
The credit assignment problem. The exploration vs. exploitation dilemma. Value Iteration algorithm. Policy Iteration algorithm. Q-learning Algorithm, Confergence of Q-learning. Temporal Difference Learning Algorithms. <\/p>\n
Recommended readings <\/p>\n
Additional Information <\/p>\n
Overview of machine learning. Why should machines learn? Operational definition of learning. <\/p>\n
Bayesian Decision Theory. Optimal Bayes Classifier. Minimum Risk Bayes Classifier. <\/p>\n
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