Deep dive into machine learning

(01) Preliminary machine learning and related mathematics
(02) Mathematical statistics and parameter estimation
(03) Matrix analysis and application (
04) Preliminary convex optimization
(05) Regression analysis and engineering application
(06) Feature engineering
(07) Workflow and model Tuning
(08) Maximum entropy model and EM algorithm
(09) Recommended system and application
(10) Clustering algorithm and application
(11) Decision tree random forest and adaboost
(12) SVM
(13) Bayesian method
(14) Topics Model
(15) Bayesian inference sampling and variation
(16) Artificial neural network
(17) Convolutional neural network
(18) Recurrent neural network and LSTM
(19) Caffe&Tensor Flow&MxNet Introduction
(20) Bayesian network and HMM
(extra Supplement) word embedding word embedding

• Chapter 1：Preliminary machine learning and related mathematics (2 hours and 11 minutes and 51 seconds)

• Chapter 2：Mathematical Statistics and Parameter Estimation (2 hours and 13 minutes and 54 seconds)

• Chapter 3：Matrix analysis and applications (2 hours and 20 minutes and 25 seconds)

• Chapter 4：Preliminary convex optimization (2 hours and 19 minutes and 0 seconds)

• Chapter 5：Regression analysis and engineering applications (2 hours and 27 minutes and 0 seconds)

• Chapter 6：feature engineering (2 hours and 58 minutes and 0 seconds)

• Chapter 7：Workflow and model tuning (2 hours and 38 minutes and 0 seconds)

• Chapter 8：Maximum entropy model and EM algorithm (2 hours and 14 minutes and 1 seconds)

• Chapter 9：Recommendation systems and applications (2 hours and 31 minutes and 0 seconds)

• Chapter 10：Clustering algorithms and applications (2 hours and 30 minutes and 0 seconds)

• Chapter 11：Decision tree random forest adaboost (2 hours and 1 minutes and 10 seconds)

• Chapter 12：SVM (2 hours and 20 minutes and 1 seconds)

• Chapter 13：Bayesian approach (2 hours and 10 minutes and 0 seconds)

• Chapter 14：topic model (1 hours and 56 minutes and 5 seconds)

• Chapter 15：Bayesian inference sampling variation method (1 hours and 30 minutes and 55 seconds)

• Chapter 16：Artificial neural networks (2 hours and 15 minutes and 35 seconds)

• Chapter 17：convolutional neural network (2 hours and 7 minutes and 1 seconds)

• Chapter 18：Recurrent Neural Networks and LSTM (1 hours and 54 minutes and 40 seconds)

• Chapter 19：Introduction to Caffe&Tensor Flow&MxNet (1 hours and 54 minutes and 8 seconds)

• Chapter 20：Bayesian Networks and HMMs (2 hours and 5 minutes and 56 seconds)

• Chapter 21：(Extra supplement) word embedding (1 hours and 33 minutes and 54 seconds)