Big data machine learning (Yuan Chun)

The "Big Data Machine Learning" course is a basic theoretical course for senior undergraduates or graduate students in the information discipline. The purpose is to cultivate students to have an in-depth understanding of the theoretical basis of big data machine learning, a firm grasp of big data machine learning methods, and the ability to solve practical problems. and other comprehensive abilities. The main contents of the course include: basic theories of statistical learning, basic methods of machine learning, and deep learning theories and methods.

• Chapter 1：Machine learning definition and typical applications (10 minutes and 2 seconds)

• Chapter 2：The relationship between machine learning and artificial intelligence (1 minutes and 34 seconds)

• Chapter 3：Commonalities and differences between deep learning methods and other artificial intelligence methods (1 minutes and 40 seconds)

• Chapter 4：The relationship between machine learning and data mining (1 minutes and 26 seconds)

• Chapter 5：The relationship between machine learning and statistical learning (1 minutes and 8 seconds)

• Chapter 6：The development history of machine learning (3 minutes and 16 seconds)

• Chapter 7：The main features of big data machine learning (9 minutes and 12 seconds)

• Chapter 8：Basic terminology of machine learning (5 minutes and 31 seconds)

• Chapter 9：supervised learning (1 minutes and 49 seconds)

• Chapter 10：hypothesis space (1 minutes and 58 seconds)

• Chapter 11：Three elements of learning methods (3 minutes and 48 seconds)

• Chapter 12：Occam's razor theorem (53 seconds)

• Chapter 13：No free lunch theorem v (3 minutes and 43 seconds)

• Chapter 14：training error and test error (49 seconds)

• Chapter 15：Overfitting and model selection (6 minutes and 8 seconds)

• Chapter 16：Generalization (1 minutes and 46 seconds)

• Chapter 17：Generative and discriminative models (1 minutes and 42 seconds)

• Chapter 18：Set aside method (3 minutes and 21 seconds)

• Chapter 19：cross validation method (36 seconds)

• Chapter 20：self-help method (2 minutes and 4 seconds)

• Chapter 21：Performance metrics (6 minutes and 23 seconds)

• Chapter 22：PR curve (4 minutes and 30 seconds)

• Chapter 23：ROC and AUC curves (1 minutes and 27 seconds)

• Chapter 24：cost sensitive error rate (2 minutes and 49 seconds)

• Chapter 25：hypothetical test (5 minutes and 17 seconds)

• Chapter 26：T test (1 minutes and 59 seconds)

• Chapter 27：bias and variance (4 minutes and 3 seconds)

• Chapter 28：Perceptron model (4 minutes and 56 seconds)

• Chapter 29：Perceptron learning strategy (2 minutes and 28 seconds)

• Chapter 30：Perceptron learning algorithm (17 minutes and 18 seconds)

• Chapter 31：Prototype cluster description (3 minutes and 43 seconds)

• Chapter 32：Performance metrics (10 minutes and 53 seconds)

• Chapter 33：Prototype clustering k-means algorithm (4 minutes and 58 seconds)

• Chapter 34：Prototype clustering learning vector algorithm (4 minutes and 55 seconds)

• Chapter 35：Prototype clustering density clustering (4 minutes and 48 seconds)

• Chapter 36：Prototype clustering hierarchical clustering (3 minutes and 22 seconds)

• Chapter 37：Overview (34 seconds)

• Chapter 38：Probabilistic graphical model (3 minutes and 18 seconds)

• Chapter 39：Bayesian network (12 minutes and 12 seconds)

• Chapter 40：Naive Bayes classifier (4 minutes and 59 seconds)

• Chapter 41：Semi-naive Bayes classifier v (24 minutes and 17 seconds)

• Chapter 42：Bayesian network structure learning inference (1 minutes and 15 seconds)

• Chapter 43：Gibbs sampling (3 minutes and 17 seconds)

• Chapter 44：Decision trees and random forests (29 seconds)

• Chapter 45：Decision tree model and basic concepts of learning (15 minutes and 29 seconds)

• Chapter 46：Information content and entropy (10 minutes and 23 seconds)

• Chapter 47：Generation of decision trees (13 minutes and 42 seconds)

• Chapter 48：Decision tree branch reduction (4 minutes and 41 seconds)

• Chapter 49：CART algorithm (15 minutes and 37 seconds)

• Chapter 50：random forest (5 minutes and 30 seconds)

• Chapter 51：Introduction to logistic regression and maximum entropy models (3 minutes and 30 seconds)

• Chapter 52：logistic regression model (7 minutes and 36 seconds)

• Chapter 53：maximum entropy model (19 minutes and 7 seconds)

• Chapter 54：Optimization method for model learning (21 minutes and 30 seconds)

• Chapter 55：SVM (46 seconds)

• Chapter 56：Introduction to SVM (6 minutes and 45 seconds)

• Chapter 57：Linearly separable support vector machine (8 minutes and 46 seconds)

• Chapter 58：Basic concepts of convex optimization problems (20 minutes and 29 seconds)

• Chapter 59：The exact definition of support vectors (3 minutes and 59 seconds)

• Chapter 60：Linear Support Vector Machine (9 minutes and 15 seconds)

• Chapter 61：Kernel methods and nonlinear SVM (3 minutes and 42 seconds)

• Chapter 62：Functional basics (21 minutes and 22 seconds)

• Chapter 63：Kernel functions and nonlinear support vector machines (14 minutes and 25 seconds)

• Chapter 64：Sequential Minimal Optimization Algorithm (16 minutes and 2 seconds)

• Chapter 65：Dimensionality reduction and metric learning (1 minutes and 59 seconds)

• Chapter 66：k nearest neighbor learning (3 minutes and 57 seconds)

• Chapter 67：Dimensionality reduction embedding (12 minutes and 26 seconds)

• Chapter 68：Principal component analysis (8 minutes and 49 seconds)

• Chapter 69：Kernelized Linear Dimensionality Reduction (5 minutes and 4 seconds)

• Chapter 70：Flow learning and metric learning (18 minutes and 15 seconds)

• Chapter 71：Boosting method adaboost algorithm (20 minutes and 38 seconds)

• Chapter 72：Training error analysis of Adaboost algorithm (4 minutes and 31 seconds)

• Chapter 73：Explanation of Adaboost algorithm (4 minutes and 38 seconds)

• Chapter 74：Implementation of Adaboost (13 minutes and 27 seconds)

• Chapter 75：EM algorithm and Gaussian mixture model (1 minutes and 29 seconds)

• Chapter 76：Questions raised (2 minutes and 23 seconds)

• Chapter 77：Introduction of EM algorithm (19 minutes and 13 seconds)

• Chapter 78：Convergence of EM algorithm (4 minutes and 16 seconds)

• Chapter 79：Application of EM algorithm in Gaussian mixture model learning (7 minutes and 11 seconds)

• Chapter 80：Promotion of EM algorithm (5 minutes and 41 seconds)

• Chapter 81：computational learning theory (1 minutes and 47 seconds)

• Chapter 82：Basic knowledge of computational learning theory (7 minutes and 27 seconds)

• Chapter 83：Probabilistic Approximate Correct Learning Theory (11 minutes and 49 seconds)

• Chapter 84：finite hypothesis space (10 minutes and 7 seconds)

• Chapter 85：VC dimension (10 minutes and 30 seconds)

• Chapter 86：learning stability (6 minutes and 12 seconds)

• Chapter 87：Hidden Markov Model (1 minutes and 10 seconds)

• Chapter 88：Basic concepts of hidden Markov models (12 minutes and 9 seconds)

• Chapter 89：Probability calculation algorithm (15 minutes and 15 seconds)

• Chapter 90：learning algorithm (10 minutes and 29 seconds)

• Chapter 91：Prediction algorithm (13 minutes and 16 seconds)

• Chapter 92：conditional random field (7 minutes and 4 seconds)

• Chapter 93：Probabilistic undirected graphical model (8 minutes and 21 seconds)

• Chapter 94：The definition and form of conditional random fields (17 minutes and 2 seconds)

• Chapter 95：Calculation problems of conditional random fields (4 minutes and 27 seconds)

• Chapter 96：Learning algorithm for conditional random fields (6 minutes and 47 seconds)

• Chapter 97：Prediction algorithm for conditional random fields (8 minutes and 13 seconds)

• Chapter 98：Learning and inference of probabilistic graphical models (2 minutes and 11 seconds)

• Chapter 99：Precise inference method: variable elimination method and belief propagation method (11 minutes and 34 seconds)

• Chapter 100：Approximate inference methods: MCMC and variational inference (20 minutes and 58 seconds)

• Chapter 101：The development history of neural networks (7 minutes and 56 seconds)

• Chapter 102：Basic concepts of neural networks and common neural networks (1) (16 minutes and 9 seconds)

• Chapter 103：Basic concepts of neural networks and common neural networks (2) (25 minutes and 23 seconds)

• Chapter 104：Boltzmann machine (6 minutes and 8 seconds)

• Chapter 105：deep learning (7 minutes and 4 seconds)

• Chapter 106：Introduction to deep learning and architecture design (6 minutes and 57 seconds)

• Chapter 107：Backpropagation Algorithm in Computational Graph Form (8 minutes and 27 seconds)

• Chapter 108：Regularization method for deep learning (1) (25 minutes and 49 seconds)

• Chapter 109：Regularization method for deep learning (2) (27 minutes and 11 seconds)

• Chapter 110：Deep learning optimization problems (18 minutes and 2 seconds)

• Chapter 111：Challenges of Neural Network Optimization (14 minutes and 33 seconds)

• Chapter 112：Neural Network Optimization Algorithm (12 minutes and 0 seconds)

• Chapter 113：Related strategies (23 minutes and 57 seconds)