إنشاء حساب
الرئيسية
الكورسات
تعلم الآلة
دورة تعلم الآلة MIT OpenCourseWare: شرح كامل لمفاهيم التعلم الآلي
21. Probabilistic Inference I

21. Probabilistic Inference I

دورة تعلم الآلة MIT OpenCourseWare: شرح كامل لمفاهيم التعلم الآلي

الدرس السابق الدرس التالي

محتوى الكورس

جميع الدروس
21 درس
49:34 16. Learning: Support Vector Machines
47:19 1. Introduction and Scope
45:58 2. Reasoning: Goal Trees and Problem Solving
49:56 3. Reasoning: Goal Trees and Rule-Based Expert Systems
48:42 4. Search: Depth-First, Hill Climbing, Beam
48:37 5. Search: Optimal, Branch and Bound, A*
48:17 6. Search: Games, Minimax, and Alpha-Beta
49:13 7. Constraints: Interpreting Line Drawings
51:32 9. Constraints: Visual Object Recognition
49:56 10. Introduction to Learning, Nearest Neighbors
49:37 11. Learning: Identification Trees, Disorder
47:54 12. Learning: Neural Nets, Back Propagation
47:16 13. Learning: Genetic Algorithms
47:49 14. Learning: Sparse Spaces, Phonology
46:54 15. Learning: Near Misses, Felicity Conditions
51:40 17. Learning: Boosting
48:58 18. Representations: Classes, Trajectories, Transitions
49:06 19. Architectures: GPS, SOAR, Subsumption, Society of Mind
48:30 21. Probabilistic Inference I
48:46 22. Probabilistic Inference II
49:31 23. Model Merging, Cross-Modal Coupling, Course Summary