加州大学伯克利分校 CS 189 统计机器学习 Introduction to Machine Learning(Spring 2021)

1.6万
10
2021-05-03 19:28:26
正在缓冲...
299
128
1523
102
加州大学伯克利分校 UC Berkeley CS 189 Introducton to Machine Learning 2021年春季统计机器学习课程(英文字幕) 课程主页:https://people.eecs.berkeley.edu/~jrs/189/
星星之火,可以燎原
视频选集
(1/25)
自动连播
Lecture 1 Introduction, Classification, Training and testing, Validation and ove
01:23:41
Lecture 2 Linear classifiers, Decision boundaries, The centroid method, Perceptr
01:29:41
Lecture 3 Gradient descent, SGD, The perceptron learning algorithm, SVM
01:31:24
Lecture 4 Soft-margin support vector machine, Features and nonlinear decision bo
01:28:11
Lecture 5 Machine learning abstractions, Optimization problem, Linear and quadra
01:35:17
Lecture 6 The Bayes decision rule and optimal risk, Generative and discriminativ
01:27:07
Lecture 7 GDA, QDA, LDA and MLE
01:41:10
Lecture 8 Eigenvectors, Eigenvalues, Eigendecomposition, The Spectral Theorem fo
01:32:39
Lecture 9 Anisotropic normal distributions, MLE, QDA, and LDA revisited for anis
01:33:02
Lecture 10 Fitting curves to data
01:43:34
Lecture 11 Newton's method, LDA vs. Logistic regression, ROC curves, Least-squar
01:30:52
Lecture 12 Statistical justifications for regression, The empirical distribution
01:32:06
Lecture 13 Ridge regression, MAP, Lasso
01:29:04
Lecture 14 Decision trees, Entropy and information gain
01:27:19
Lecture 15 More decision trees, decision tree regression, Stopping early, Prunin
01:37:53
Lecture 16 Kernels, Kernel ridge regression, The polynomial kernel, Kernel perce
01:35:16
Lecture 17 Neural networks, Gradient descent and Backpropagation algorithm
01:31:35
Lecture 18 Neuron biology, Neuronal computational models, Backpropagation with s
01:25:39
Lecture 19 Heuristics for faster training, avoid bad local minima, avoid overfit
01:30:54
Lecture 20 Unsupervised learning, PCA, Eigenfaces for face recognition
01:29:41
Lecture 21 SVD and its application to PCA, K-means clustering, K-medoids cluster
01:27:38
Lecture 22 Spectral graph partitioning and graph clustering, Continuous Optimiza
01:25:05
Lecture 23 Graph clustering with multiple eigenvectors. The geometry of high-dim
01:27:43
Lecture 24 AdaBoost, a boosting method for ensemble learning. Nearest neighbor c
01:25:09
Lecture 25 The exhaustive algorithm for k-nearest neighbor queries. Speeding up
01:34:44
客服
顶部
赛事库 课堂 2021拜年纪