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Week - 1 |
History and Definition of AI, Foundations of Intelligent Behavior, Agent Types (Simple, Model-Based, Goal-Based, Utility-Based), Agent Environments and Their Properties. |
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Week - 2 |
Problem Formulation, Search Trees and Graphs, Breadth-First Search, Depth-First Search, Iterative Deepening Search. |
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Week - 3 |
Informed (Heuristic) Search Strategies, Greedy Best-First Search, A* Algorithm, Properties of Heuristic Functions (Admissibility, Consistency). |
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Week - 4 |
Definition and Formulation of Constraint Satisfaction Problems, Backtracking Algorithm, Inference (Forward Checking, ARC Consistency), Problem Structure Analysis. |
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Week - 5 |
Game Theory and the Minimax Algorithm, Alpha-Beta Pruning, Decision-Making Under Uncertainty, Basic Concepts of Probability Theory, Bayes' Rule. |
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Week - 6 |
Architecture of Expert Systems (Knowledge Base, Inference Engine), Rule-Based Systems, Logic (Logical Inference, First-Order Logic), Semantic Networks and Frames. |
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Week - 7 |
Machine Learning Paradigms, Types of Learning, Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score), Train/Test/Validation Data Split, Cross-Validation. |
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Week - 8 |
Linear Regression, Multiple Linear Regression, Polynomial Regression, Overfitting and Model Complexity, Regularization (Ridge, Lasso). |
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Week - 9 |
Logistic Regression, Decision Trees, Support Vector Machines (SVM), Naive Bayes Classifier. |
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Week - 10 |
Introduction to Cluster Analysis, K-Means Algorithm, Hierarchical Clustering, Density-Based Clustering (DBSCAN), Cluster Validity Metrics. |
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Week - 11 |
The Curse of Dimensionality, Principal Component Analysis (PCA), Association Rule Learning, Apriori Algorithm, Concepts of Confidence and Support. |
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Week - 12 |
Fundamentals of Reinforcement Learning (Agent, Environment, Reward), Markov Decision Processes, Introduction to Artificial Neural Networks, Single-Layer Perceptron, Activation Functions. |
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Week - 13 |
Multi-Layer Perceptrons, Backpropagation Algorithm, Overview of Deep Learning, A Brief Look at Image Processing and CNNs, A Brief Look at Natural Language Processing and RNNs. |
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Week - 14 |
AI in Robotics: Perception, Planning, Control. AI Ethics: Bias and Fairness, Transparency and Explainability, Privacy, The Future of Work, Social Impact and Responsibilities. |
Eskisehir Technical University Info Package