Sina Tootoonian
Welcome to my channel! My aim here is to help democratize machine learning by going through a classic textbook in the field, Bishop's "Pattern Recognition and Machine Learning." Each week I post a video where I read through a section and discuss the important ideas, pitfalls, what's (in my experience) useful, and what can be skipped. I hope this way to show you that if you've got a basic background in a quantitative field, like math, physics, engineering, or computer science, you can learn this material, and gain a deeper understanding of machine learning.
Chapter 4 Summary - Pattern Recognition and Machine Learning
4.5.2 Байесовская логистическая регрессия – предиктивное распределение – PRML
4.5 и 4.5.1 Байесовская логистическая регрессия – приближение Лапласа – PRML
4.4.1 Сравнение моделей и BIC — распознавание образов и машинное обучение
4.4 Приближение Лапласа — распознавание образов и машинное обучение
4.3.6 Canonical Link Functions - Pattern Recognition and Machine Learning
4.3.5 Probit Regression - Pattern Recognition and Machine Learning
4.3.4 Multi-class Logistic Regression - Pattern Recognition and Machine Learning
What's REALLY happening in IRLS for Logistic Regression
4.3.3 Iterative Reweighted Least Squares - Pattern Recognition and Machine Learning
4.3.2 Logistic Regression - Pattern Recognition and Machine Learning
4.3.0/1 Probabilistic Discriminative Models: Fixed Basis Functions - PRML
4.2.3/4 Discrete Features / Exponential Family - Pattern Recognition and Machine Learning
4.2.2 Probabilistic Generative Models - Maximum Likelihood Solution - PRML
4.2.1 Continuous Inputs - Pattern Recognition and Machine Learning
4.2 Probabilistic Generative Models - Pattern Recognition and Machine Learning
4.1.7 The Perceptron Algorithm - Pattern Recognition and Machine Learning
4.1.6 Fisher's Discriminant for Multiple Classes - Pattern Recognition and Machine Learning
4.1.5 Relation to least squares - Pattern Recognition and Machine Learning
4.1.4 Fisher's Linear Discriminant - Pattern Recognition and Machine Learning
4.1.3 Least Squares for Classification - Pattern Recognition and Machine Learning
4.1.1 and 4.1.2: Discriminant Functions - Two / multiple classes - PRML
Chapter 4 - Linear Models for Classification - Pattern Recognition and Machine Learning
12.4.2 Autoassociative Neural Networks - Pattern Recognition and Machine Learning
12.4.1 Independent Component Analysis - Pattern Recognition and Machine Learning
12.3 Kernel PCA - Pattern Recognition and Machine Learning
12.2.4 Factor Analysis - Pattern Recognition and Machine Learning
12.2.3 Bayesian PCA - Pattern Recognition and Machine Learning
12.2.2 EM algorithm for PCA - Pattern Recognition and Machine Learning
Understanding Expectation Maximization as Coordinate Ascent