01
Lecture 1· 49:34
Learning: Support Vector Machines
Patrick Winston's MIT lecture on Support Vector Machines, delving into the mathematical details of maximizing street width and the application of kernel functions.
49:34
Learning: Boosting Algorithms
Patrick Winston's MIT lecture on boosting algorithms, examining how multiple weak classifiers can be combined to form a strong predictive model.
4lectures3htotal runtime
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Syllabus
402
Lecture 2· 51:40
Learning: Boosting Algorithms
Patrick Winston's MIT lecture on boosting algorithms, examining how multiple weak classifiers can be combined to form a strong predictive model.
51:40
03
Lecture 3· 46:54
Learning: Near Misses and Felicity Conditions in AI
Explore the mechanisms of one-shot learning in AI, focusing on how systems learn from 'near misses' and the 'felicity conditions' that enable effective knowledge acquisition.
46:54
04
Lecture 4· 47:49
Learning: Sparse Spaces and Phonological Generalization
Investigate how AI systems generalize from sparse data, using the example of phonological rules in language to illustrate learning in distinctive feature spaces.
47:49