השוואת שיטות

	למידת תוכנית לימודים (Curriculum Learning)	למידה פעילה	למידת ריבוי משימות	Transfer Learning
תחום	למידה עמוקה	למידת מכונה	למידה עמוקה	למידת מכונה
משפחה	Machine learning	Machine learning	Machine learning	Machine learning
שנת המקור	2009	2009	1997	2010 (formalized); 1990s (early roots)
הוגה השיטה	Yoshua Bengio et al.	Burr Settles	Rich Caruana	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
סוג	Training strategy	Interactive supervised learning framework	Inductive transfer method	Learning paradigm
מקור מכונן	Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. International Conference on Machine Learning (ICML), 41–48.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
כינויים	Scheduled Training, Difficulty-Based Training, Self-Paced Learning, Müfredat Öğrenimi	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	MTL, Joint Learning, Shared Representation Learning, Çok Görevli Öğrenme	TL, domain adaptation, fine-tuning, pre-trained model adaptation
קשורות	3	2	3	3
תקציר	Curriculum Learning is a training strategy for machine learning models, introduced by Bengio et al. in 2009, in which training examples are presented in a meaningful order—typically from easy to hard—rather than at random. Inspired by how humans and animals learn progressively, it organizes training data into a curriculum that starts with simpler, cleaner, or more representative samples and gradually introduces harder or more complex examples as the model matures.	Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.	Multitask Learning (MTL) is a machine learning paradigm in which a model is trained simultaneously on multiple related tasks, sharing representations across them to improve generalization. Introduced formally by Rich Caruana in 1997, MTL draws on the intuition that auxiliary tasks act as inductive bias, providing extra supervision signals that help the shared layers learn richer, more robust feature representations than single-task training would yield.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.