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Machine Learning algorithms explained, with Vadim Smolyakov - HS#18
Manage episode 432622723 series 3558558
Contenido proporcionado por Miko Pawlikowski. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Miko Pawlikowski o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
Simplifying Algorithms with Vadim Smolyakov!
Get Vadim's book 45% OFF with code hockeystick24 here: https://mng.bz/4J5Q
Join Miko Pawlikowski on HockeyStick as he discusses machine learning algorithms with Vadim Smolyakov, author of 'Machine Learning Algorithms in Depth.' They explore Vadim's experiences at MIT CSAIL, his work at Microsoft, and key machine learning concepts like Bayesian nonparametrics, decision trees, and Markov chain Monte Carlo methods. Vadim also shares insights on his book, the challenges in implementing ML algorithms, and predictions about the future of AI. This episode is perfect for intermediate learners and those new to machine learning.
0:00 Guest Introduction: Vadim Smolyakov
00:48 MIT CSAIL Experience
01:28 Bayesian Inference and Non-Parametrics
02:30 Vadim's Work at Microsoft
03:14 The Origin of Vadim's Book
06:41 Target Audience for the Book
08:04 Explaining Bayesian Algorithms
15:57 Supervised vs Unsupervised Learning
19:22 Decision Trees and Random Forests
24:42 Challenges in Implementing ML Algorithms
31:32 Top Machine Learning Algorithms
45:27 Future of AI and ML
50:31 Conclusion and Farewell
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.hockeystick.show
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