CS-330: Artificial intelligence

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Lectures in this course (13)

Artificial Intelligence Fundamentals

Covers artificial intelligence fundamentals, emphasizing practical applications and programming exercises.

Knowledge Representation: Introduction

Covers knowledge representation in AI, logical inference, and applications in various domains.

Inference Engines: Resolution and Horn Clauses

Covers inference engines based on resolution, Horn clauses, filtering, and unification in artificial intelligence.

Expert Systems: Backward Chaining

Explores expert systems, backward chaining, and uncertainty through fuzzy logic in practical applications.

Uncertain Reasoning: Bayesian Networks

Explores uncertain reasoning, Bayesian networks, and stochastic resolution, emphasizing the importance of probabilistic logic and abduction.

Search Algorithms: Abductive Reasoning

Explores abductive reasoning, search algorithms, and heuristic search for problem-solving.

Constraint Satisfaction: Formulation and Algorithms

Covers the formulation of constraint satisfaction problems and systematic algorithms for solving them efficiently.

Diagnostic: Abduction and Consistency

Explores diagnostic problems, emphasizing abduction and consistency in finding faulty components based on observed symptoms and measurements.

Automated Planning: Modeling and Constraints

Explores automated planning, constraints, and applications in diverse domains, emphasizing the challenges and efficiency of solving planning problems.

Supervised Learning: Classification and Regression

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Structured Classifications: Decision Trees and Boosting

Explores decision trees, overfitting elimination, boosting techniques, and their practical applications in predictive modeling.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Bio-Inspired Learning: Neural Networks, Genetic Algorithms

Explores bio-inspired learning with neural networks and genetic algorithms, covering structure, training, and practical applications.

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