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<title>مبانی برنامه نویسی | Lecture 1: Introduction to Graphical Models</title>
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<title>مبانی برنامه نویسی | Lecture 2: Bayesian Networks</title>
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<title>مبانی برنامه نویسی | Lecture 3: Undirected Graphical Models</title>
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<title>مبانی برنامه نویسی | Lecture 4: Exact Inference</title>
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<title>مبانی برنامه نویسی | Lecture 5: Parameter Estimation in Fully Observed Bayesian Networks</title>
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<title>مبانی برنامه نویسی | Lecture 6: Learning Partially Observed GM and the EM Algorithm</title>
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<title>مبانی برنامه نویسی | Lecture 7: Maximum likelihood learning of undirected GM</title>
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<title>مبانی برنامه نویسی | Lecture 8: Causal Discovery and Inference</title>
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<title>مبانی برنامه نویسی | Lecture 9: Modeling Networks</title>
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<title>مبانی برنامه نویسی | Lecture 10: Sequential Models</title>
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<title>مبانی برنامه نویسی | Lecture 11: Kalman Filtering and Topic Models</title>
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<title>مبانی برنامه نویسی | Lecture 12: Theory of Variational Inference: Marginal Polytope, Inner and Outer Approximation</title>
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<title>مبانی برنامه نویسی | Lecture 13: Approximate Inference: Monte Carlo and Sequential Monte Carlo Methods</title>
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<title>مبانی برنامه نویسی | Lecture 14: Approximate Inference: Markov Chain Monte Carlo</title>
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<title>مبانی برنامه نویسی | Lecture 15: Statistical and Algorithmic Foundations of Deep Learning</title>
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<title>مبانی برنامه نویسی | Lecture 16: Building Blocks of Deep Learning</title>
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<title>مبانی برنامه نویسی | Lecture 17: Deep generative models (part 1)</title>
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<title>مبانی برنامه نویسی | Lecture 18: Deep Generative Models (Part 2)</title>
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<title>مبانی برنامه نویسی | Lecture 19: Case Study: Text Generation</title>
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<title>مبانی برنامه نویسی | Lecture 20: Reinforcement Learning & Control Through Inference in GM</title>
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<title>مبانی برنامه نویسی | Lecture 21: Sequential decision making (part 2): The algorithms</title>
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<title>مبانی برنامه نویسی | Lecture 22: Bayesian non-parametrics</title>
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<title>مبانی برنامه نویسی | Lecture 23: Bayesian non-parametrics (continued)</title>
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<title>مبانی برنامه نویسی | Lecture 24: Integrative Paradigms of GM: Regularized Bayesian Methods</title>
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<title>مبانی برنامه نویسی | Lecture 25: Spectral Learning for Graphical Models</title>
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<title>مبانی برنامه نویسی | Lecture 27: Scalable Algorithms and Systems for Learning, Inference, and Prediction</title>
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<title>مبانی برنامه نویسی | Lecture 28: A Civil Engineering Perspective on AI</title>
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