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Training

Pattern Recognition Cai Xuanping, National University of Defense Technology

BayesianclusteringPattern Recognition

In terms of its academic connotation, pattern recognition is a subject of data processing and information analysis. In terms of its application characteristics, it belongs to the categories of artificial intelligence and machine learning. The pattern recognition course is a professional basic course for undergraduates in information engineering and related majors. It is also an elective course for many other majors and occupies a very important position in the knowledge structure. It is of great significance for consolidating learned knowledge, carrying out professional course study and future work.

Total of 35 lessons22 hours and 6 minutes and 8 seconds

Deep dive into machine learning

Neural NetworksMachine LearningBayesianclustering

(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) Topic 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

Total of 21 lessons1 days and 22 hours and 12 minutes and 36 seconds