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Week - 1 |
Introduction to Artificial Intelligence (AI). |
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Week - 2 |
Intelligent Agents & Solving Problems by Searching. |
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Week - 3 |
Informed Search & Search in Complex Environments. |
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Week - 4 |
Constraint Satisfaction Problems & Adversarial Search and Games. |
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Week - 5 |
Logical Agents & First-Order Logic. |
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Week - 6 |
Probabilistic Reasoning & Bayes Nets. |
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Week - 7 |
Machine Learning, Types of Learning, and Data Preparation. |
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Week - 8 |
Cross Validation, Model Evaluation & Overfitting. |
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Week - 9 |
Supervised Learning: Regression, Decision Trees, Naive Bayes, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM). |
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Week - 10 |
Unsupervised Learning: Introduction to Cluster Analysis, K-Means Algorithm, Density-Based Clustering (DBSCAN), Cluster Validity Metrics. |
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Week - 11 |
Reinforcement Learning: Reward Functions, Basics of Q-Learning. |
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Week - 12 |
Artificial Neural Networks & Perceptron: Biological vs. Artificial Neurons, Perceptron Learning Rule, Multi-Layer Networks. |
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Week - 13 |
Deep Learning: Deep Architectures, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN). |
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Week - 14 |
Recent Trends in Artificial Intelligence: Generative AI, AI Ethics, and Future Directions. |
Eskisehir Technical University Info Package