打包8套机器学习课程

2014斯坦福大学机器学习mkv视频 [5.03G]

pdf [39.47M]

lecture1.pdf [3.30M]

lecture10.pdf [1.48M]

lecture11.pdf [497.64K]

lecture12.pdf [2.30M]

lecture13.pdf [2.17M]

lecture14.pdf [1.61M]

lecture15.pdf [3.33M]

lecture16.pdf [1.42M]

lecture17.pdf [1.98M]

lecture18.pdf [1.97M]

lecture2.pdf [2.88M]

lecture3.pdf [1.80M]

lecture4.pdf [1.70M]

lecture5.pdf [242.37K]

lecture6.pdf [2.12M]

lecture7.pdf [2.34M]

lecture8.pdf [4.97M]

lecture9.pdf [3.37M]

ppt [107.45M]

lecture1.pptx [4.02M]

lecture10.pptx [3.35M]

lecture11.pptx [1.93M]

lecture12.pptx [5.39M]

lecture13.pptx [2.79M]

lecture14.pptx [3.62M]

lecture15.pptx [6.05M]

lecture16.pptx [3.60M]

lecture17.pptx [3.78M]

lecture18.pptx [6.13M]

lecture2.pptx [5.35M]

lecture3.pptx [4.92M]

lecture4.pptx [4.40M]

lecture5.pptx [407.28K]

lecture6.pptx [3.82M]

lecture7.pptx [2.59M]

lecture8.pptx [40.36M]

lecture9.pptx [4.96M]

机器学习课程2014源代码 [65.84M]

mlclass-ex1-jin [42.77K]

computecost.m [0.68K]

computecostmulti.m [0.69K]

ex1.m [3.36K]

ex1_multi.m [4.38K]

ex1data1.txt [1.33K]

ex1data2.txt [0.64K]

featurenormalize.m [1.44K]

gradientdescent.m [1.14K]

gradientdescentmulti.m [0.96K]

ml_login_data.mat [0.26K]

normaleqn.m [0.66K]

ogldpf.log

plotdata.m [1.03K]

submit.m [15.22K]

submitweb.m [10.47K]

warmupexercise.m [0.51K]

mlclass-ex2-jin [224.48K]

costfunction.m [1.02K]

costfunctionreg.m [1.14K]

ex2.m [3.65K]

ex2.pdf [188.26K]

ex2_reg.m [2.90K]

ex2data1.txt [3.69K]

ex2data2.txt [2.18K]

mapfeature.m [0.50K]

plotdata.m [0.98K]

plotdecisionboundary.m [1.42K]

predict.m [0.82K]

sigmoid.m [0.44K]

submit.m [16.69K]

submitweb.m [0.79K]

mlclass-ex3-jin [7.60M]

displaydata.m [1.47K]

ex3.m [2.09K]

ex3.pdf [327.73K]

ex3_nn.m [2.58K]

ex3data1.mat [7.16M]

ex3weights.mat [77.73K]

fmincg.m [8.54K]

lrcostfunction.m [1.90K]

onevsall.m [2.16K]

predict.m [1.26K]

predictonevsall.m [1.53K]

sigmoid.m [0.13K]

submit.m [16.64K]

submitweb.m [0.79K]

mlclass-ex4-jin [7.68M]

checknngradients.m [1.90K]

computenumericalgradient.m [1.07K]

debuginitializeweights.m [0.82K]

displaydata.m [1.47K]

ex4.m [7.88K]

ex4.pdf [406.78K]

ex4data1.mat [7.16M]

ex4weights.mat [77.73K]

fmincg.m [8.54K]

nncostfunction.m [5.32K]

predict.m [0.57K]

randinitializeweights.m [0.96K]

sigmoid.m [0.13K]

sigmoidgradient.m [0.70K]

submit.m [16.73K]

submitweb.m [0.79K]

mlclass-ex5-jin [224.43K]

ex5.m [7.18K]

ex5.pdf [181.55K]

ex5data1.mat [1.29K]

featurenormalize.m [0.50K]

fmincg.m [8.54K]

learningcurve.m [2.53K]

linearregcostfunction.m [1.11K]

plotfit.m [0.79K]

polyfeatures.m [0.69K]

submit.m [16.81K]

submitweb.m [0.79K]

trainlinearreg.m [0.70K]

validationcurve.m [1.96K]

mlclass-ex6-jin [975.41K]

dataset3params.m [1.92K]

emailfeatures.m [2.07K]

emailsample1.txt [0.38K]

emailsample2.txt [1.27K]

ex6.m [4.03K]

ex6.pdf [355.43K]

ex6_spam.m [4.49K]

ex6data1.mat [0.96K]

ex6data2.mat [7.43K]

ex6data3.mat [5.90K]

gaussiankernel.m [0.71K]

getvocablist.m [0.74K]

linearkernel.m [0.32K]

plotdata.m [0.56K]

porterstemmer.m [9.67K]

processemail.m [3.87K]

readfile.m [0.39K]

spamsample1.txt [0.64K]

spamsample2.txt [0.24K]

spamtest.mat [110.08K]

spamtrain.mat [418.76K]

submit.m [16.44K]

submitweb.m [0.79K]

svmpredict.m [1.63K]

svmtrain.m [5.82K]

visualizeboundary.m [0.72K]

visualizeboundarylinear.m [0.40K]

vocab.txt [19.77K]

mlclass-ex7-jin [11.36M]

bird_small.mat [44.54K]

bird_small.png [32.26K]

computecentroids.m [1.25K]

displaydata.m [1.47K]

drawline.m [0.23K]

ex7.m [5.43K]

ex7.pdf [742.07K]

ex7_pca.m [7.06K]

ex7data1.mat [0.97K]

ex7data2.mat [4.67K]

ex7faces.mat [10.52M]

featurenormalize.m [0.50K]

findclosestcentroids.m [1.15K]

kmeansinitcentroids.m [0.78K]

pca.m [0.83K]

plotdatapoints.m [0.42K]

plotprogresskmeans.m [0.82K]

projectdata.m [0.93K]

recoverdata.m [1.00K]

runkmeans.m [1.93K]

submit.m [16.56K]

submitweb.m [0.79K]

mlclass-ex8-jin [873.97K]

checkcostfunction.m [1.58K]

coficostfunc.m [2.19K]

computenumericalgradient.m [1.07K]

estimategaussian.m [0.95K]

ex8.m [3.72K]

ex8.pdf [264.67K]

ex8_cofi.m [6.93K]

ex8_movieparams.mat [196.48K]

ex8_movies.mat [218.16K]

ex8data1.mat [9.28K]

ex8data2.mat [91.29K]

fmincg.m [8.54K]

loadmovielist.m [0.64K]

movie_ids.txt [47.31K]

multivariategaussian.m [0.79K]

normalizeratings.m [0.47K]

selectthreshold.m [1.45K]

submit.m [17.10K]

submitweb.m [0.79K]

visualizefit.m [0.57K]

整合pdf [8.00M]

ex1.pdf [509.33K]

ex2.pdf [188.26K]

ex3.pdf [327.73K]

ex4.pdf [406.78K]

ex5.pdf [181.55K]

ex6.pdf [355.43K]

ex7.pdf [742.07K]

ex8.pdf [264.67K]

programming exercise(机器学习2014练习).pdf [2.58M]

源代码打印.pdf [2.49M]

源代码目录.docx [23.49K]

.gitattributes [0.47K]

.gitignore [2.58K]

coursera作业答案 仅供参考.zip [28.90M]

readme.md [1.43K]

教程和笔记 [215.43M]

机器学习个人笔记完整版2.5_kindle7寸(1).pdf [6.66M]

机器学习个人笔记完整版v4.11.epub [28.40M]

机器学习个人笔记完整版v4.21.pdf [11.30M]

课程答案（比以前版本更全面的答案）.zip [169.07M]

推荐播放器 [19.22M]

potplayer_1.6.51270.zip [19.22M]

网易视频教程 [3.27G]

1.mp4 [152.97M]

10.mp4 [162.39M]

11.mp4 [183.36M]

12.mp4 [165.53M]

13.mp4 [166.75M]

14.mp4 [179.15M]

15.mp4 [172.08M]

16.mp4 [162.48M]

17.mp4 [163.23M]

18.mp4 [170.51M]

19.mp4 [168.99M]

2.mp4 [235.94M]

20.mp4 [170.36M]

3.mp4 [150.52M]

4.mp4 [148.19M]

5.mp4 [152.97M]

6.mp4 [148.04M]

7.mp4 [153.48M]

8.mp4 [172.01M]

9.mp4 [165.36M]

教程目录.txt [0.54K]

1 – 1 – welcome (7 min).mkv [11.69M]

1 – 2 – what is machine learning_ (7 min).mkv [9.25M]

1 – 3 – supervised learning (12 min).mkv [13.25M]

1 – 4 – unsupervised learning (14 min).mkv [16.45M]

10 – 1 – deciding what to try next (6 min).mkv [6.78M]

10 – 2 – evaluating a hypothesis (8 min).mkv [8.36M]

10 – 3 – model selection and train_validation_test sets (12 min).mkv [14.92M]

10 – 4 – diagnosing bias vs. variance (8 min).mkv [8.86M]

10 – 5 – regularization and bias_variance (11 min).mkv [12.42M]

10 – 6 – learning curves (12 min).mkv [12.74M]

10 – 7 – deciding what to do next revisited (7 min).mkv [8.08M]

11 – 1 – prioritizing what to work on (10 min).mkv [11.03M]

11 – 2 – error analysis (13 min).mkv [15.22M]

11 – 3 – error metrics for skewed classes (12 min).mkv [13.07M]

11 – 4 – trading off precision and recall (14 min).mkv [15.77M]

11 – 5 – data for machine learning (11 min).mkv [12.70M]

12 – 1 – optimization objective (15 min).mkv [16.42M]

12 – 2 – large margin intuition (11 min).mkv [11.65M]

12 – 3 – mathematics behind large margin classification (optional) (20 min).mkv [21.51M]

12 – 4 – kernels i (16 min).mkv [17.32M]

12 – 5 – kernels ii (16 min).mkv [17.20M]

12 – 6 – using an svm (21 min).mkv [23.63M]

13 – 1 – unsupervised learning_ introduction (3 min).mkv [3.76M]

13 – 2 – k-means algorithm (13 min).mkv [13.61M]

13 – 3 – optimization objective (7 min)(1).mkv [8.04M]

13 – 3 – optimization objective (7 min).mkv [8.03M]

13 – 4 – random initialization (8 min).mkv [8.56M]

13 – 5 – choosing the number of clusters (8 min).mkv [9.28M]

14 – 1 – motivation i_ data compression (10 min).mkv [14.15M]

14 – 2 – motivation ii_ visualization (6 min).mkv [6.22M]

14 – 3 – principal component analysis problem formulation (9 min).mkv [10.32M]

14 – 4 – principal component analysis algorithm (15 min).mkv [17.55M]

14 – 5 – choosing the number of principal components (11 min).mkv [11.67M]

14 – 6 – reconstruction from compressed representation (4 min).mkv [4.92M]

14 – 7 – advice for applying pca (13 min).mkv [14.50M]

15 – 1 – problem motivation (8 min).mkv [8.23M]

15 – 2 – gaussian distribution (10 min).mkv [11.53M]

15 – 3 – algorithm (12 min).mkv [13.77M]

15 – 4 – developing and evaluating an anomaly detection system (13 min).mkv [14.96M]

15 – 5 – anomaly detection vs. supervised learning (8 min).mkv [9.17M]

15 – 6 – choosing what features to use (12 min).mkv [13.93M]

15 – 7 – multivariate gaussian distribution (optional) (14 min).mkv [15.72M]

15 – 8 – anomaly detection using the multivariate gaussian distribution (optional) (14 min).mkv [16.12M]

16 – 1 – problem formulation (8 min).mkv [10.57M]

16 – 2 – content based recommendations (15 min).mkv [16.71M]

16 – 3 – collaborative filtering (10 min).mkv [11.60M]

16 – 4 – collaborative filtering algorithm (9 min).mkv [10.18M]

16 – 5 – vectorization_ low rank matrix factorization (8 min).mkv [9.55M]

16 – 6 – implementational detail_ mean normalization (9 min).mkv [9.58M]

17 – 1 – learning with large datasets (6 min).mkv [6.41M]

17 – 2 – stochastic gradient descent (13 min).mkv [15.12M]

17 – 3 – mini-batch gradient descent (6 min).mkv [7.22M]

17 – 4 – stochastic gradient descent convergence (12 min).mkv [13.15M]

17 – 5 – online learning (13 min).mkv [14.72M]

17 – 6 – map reduce and data parallelism (14 min).mkv [15.84M]

18 – 1 – problem description and pipeline (7 min).mkv [7.81M]

18 – 2 – sliding windows (15 min).mkv [16.30M]

18 – 3 – getting lots of data and artificial data (16 min).mkv [18.57M]

18 – 4 – ceiling analysis_ what part of the pipeline to work on next (14 min).mkv [15.90M]

19 – 1 – summary and thank you (5 min).mkv [6.02M]

2 – 1 – model representation (8 min).mkv [8.86M]

2 – 2 – cost function (8 min).mkv [8.91M]

2 – 3 – cost function – intuition i (11 min).mkv [12.06M]

2 – 4 – cost function – intuition ii (9 min).mkv [11.22M]

2 – 5 – gradient descent (11 min).mkv [13.32M]

2 – 6 – gradient descent intuition (12 min).mkv [12.84M]

2 – 7 – gradientdescentforlinearregression (6 min).mkv [12.02M]

2 – 8 – what_s next (6 min).mkv [5.99M]

3 – 1 – matrices and vectors (9 min).mkv [9.42M]

3 – 2 – addition and scalar multiplication (7 min).mkv [7.35M]

3 – 3 – matrix vector multiplication (14 min).mkv [14.78M]

3 – 4 – matrix matrix multiplication (11 min).mkv [12.42M]

3 – 5 – matrix multiplication properties (9 min).mkv [9.67M]

3 – 6 – inverse and transpose (11 min).mkv [12.69M]

4 – 1 – multiple features (8 min).mkv [8.71M]

4 – 2 – gradient descent for multiple variables (5 min).mkv [5.71M]

4 – 3 – gradient descent in practice i – feature scaling (9 min).mkv [9.32M]

4 – 4 – gradient descent in practice ii – learning rate (9 min).mkv [9.13M]

4 – 5 – features and polynomial regression (8 min).mkv [8.15M]

4 – 6 – normal equation (16 min).mkv [16.88M]

4 – 7 – normal equation noninvertibility (optional) (6 min).mkv [6.15M]

5 – 1 – basic operations (14 min).mkv [17.50M]

5 – 2 – moving data around (16 min).mkv [20.52M]

5 – 3 – computing on data (13 min).mkv [15.04M]

5 – 4 – plotting data (10 min).mkv [13.17M]

5 – 5 – control statements_ for, while, if statements (13 min).mkv [16.29M]

5 – 6 – vectorization (14 min).mkv [15.88M]

5 – 7 – working on and submitting programming exercises (4 min).mkv [5.41M]

6 – 1 – classification (8 min).mkv [8.65M]

6 – 2 – hypothesis representation (7 min).mkv [8.23M]

6 – 3 – decision boundary (15 min).mkv [16.51M]

6 – 4 – cost function (11 min).mkv [12.92M]

6 – 5 – simplified cost function and gradient descent (10 min).mkv [11.80M]

6 – 6 – advanced optimization (14 min).mkv [17.95M]

6 – 7 – multiclass classification_ one-vs-all (6 min).mkv [6.83M]

7 – 1 – the problem of overfitting (10 min).mkv [11.00M]

7 – 2 – cost function (10 min).mkv [11.48M]

7 – 3 – regularized linear regression (11 min).mkv [11.84M]

7 – 4 – regularized logistic regression (9 min).mkv [10.77M]

8 – 1 – non-linear hypotheses (10 min).mkv [10.73M]

8 – 2 – neurons and the brain (8 min).mkv [9.77M]

8 – 3 – model representation i (12 min).mkv [13.32M]

8 – 4 – model representation ii (12 min).mkv [13.27M]

8 – 5 – examples and intuitions i (7 min).mkv [7.78M]

8 – 6 – examples and intuitions ii (10 min).mkv [13.84M]

8 – 7 – multiclass classification (4 min).mkv [4.77M]

9 – 1 – cost function (7 min).mkv [7.56M]

9 – 2 – backpropagation algorithm (12 min).mkv [13.75M]

9 – 3 – backpropagation intuition (13 min).mkv [15.25M]

9 – 4 – implementation note_ unrolling parameters (8 min).mkv [9.27M]

9 – 5 – gradient checking (12 min).mkv [13.32M]

9 – 6 – random initialization (7 min).mkv [7.46M]

9 – 7 – putting it together (14 min).mkv [16.10M]

9 – 8 – autonomous driving (7 min).mkv [14.79M]

机器学习导论_42_上海交大(张志华) [34.22G]

1 基本概念.mp4 [833.42M]

10 核定义.mp4 [840.46M]

11 正定核性质.mp4 [732.40M]

12 正定核应用.mp4 [766.98M]

13 核主元分析.mp4 [836.17M]

14 主元分析.mp4 [854.21M]

15 主坐标分析.mp4 [732.18M]

16 期望最大算法.mp4 [717.03M]

17 概率pca.mp4 [659.33M]

18 最大似然估计方法.mp4 [747.24M]

19 em算法收敛性.mp4 [911.58M]

2 随机向量.mp4 [783.70M]

20 mds方法.mp4 [993.05M]

21 mds中加点方法.mp4 [650.00M]

22 矩阵次导数.mp4 [684.66M]

23 矩阵范数.mp4 [822.33M]

24 次导数.mp4 [783.83M]

25 spectral clustering.mp4 [620.10M]

26 k-means algorithm.mp4 [802.07M]

27 matr-x completion.mp4 [737.15M]

28 fisher判别分析.mp4 [918.01M]

29 谱聚类1 .mp4 [955.05M]

3 随机向量性质.mp4 [716.79M]

30 谱聚类2.mp4 [997.68M]

31 computational methods1.mp4 [904.69M]

32 computational methods2.mp4 [980.74M]

33 fisher discriminant analysis.mp4 [976.99M]

34 kernel fda.mp4 [968.28M]

35 linear classification1.mp4 [962.55M]

36 linear classification2.mp4 [987.11M]

37 naive bayes方法.mp4 [988.37M]

38 support vector machines1.mp4 [962.00M]

39 support vector machines2.mp4 [931.91M]

4 多元高斯分布.mp4 [768.81M]

40 svm.mp4 [932.42M]

41 boosting1.mp4 [978.84M]

42 boosting2.mp4 [981.56M]

5 分布性质.mp4 [561.94M]

6 条件期望.mp4 [789.45M]

7 多项式分布.mp4 [800.88M]

8 多元高斯分布及应用.mp4 [745.73M]

9 渐近性质.mp4 [727.84M]

机器学习基石_国立台湾大学(林轩田) [887.84M]

1 – 1 – course introduction (10-58)(1).mp4 [13.79M]

1 – 2 – what is machine learning (18-28).mp4 [15.94M]

1 – 3 – applications of machine learning (18-56)(1).mp4 [22.31M]

1 – 4 – components of machine learning (11-45)(1).mp4 [10.66M]

1 – 5 – machine learning and other fields (10-21)(1).mp4 [11.97M]

10 – 1 – logistic regression problem (14-33).mp4 [11.94M]

10 – 2 – logistic regression error (15-58).mp4 [11.96M]

10 – 3 – gradient of logistic regression error (15-38).mp4 [12.37M]

10 – 4 – gradient descent (19-18)(1).mp4 [14.91M]

11 – 1 – linear models for binary classification (21-35).mp4 [16.91M]

11 – 2 – stochastic gradient descent (11-39).mp4 [9.96M]

11 – 3 – multiclass via logistic regression (14-18).mp4 [11.28M]

11 – 4 – multiclass via binary classification (11-35).mp4 [9.36M]

12 – 1 – quadratic hypothesis (23-47).mp4 [17.92M]

12 – 2 – nonlinear transform (09-52).mp4 [8.03M]

12 – 3 – price of nonlinear transform (15-37).mp4 [12.55M]

12 – 4 – structured hypothesis sets (09-36).mp4 [7.31M]

13 – 1 – what is overfitting- (10-45).mp4 [9.01M]

13 – 2 – the role of noise and data size (13-36).mp4 [11.40M]

13 – 3 – deterministic noise (14-07).mp4 [11.92M]

13 – 4 – dealing with overfitting (10-49).mp4 [8.81M]

14 – 1 – regularized hypothesis set (19-16).mp4 [15.18M]

14 – 2 – weight decay regularization (24-08).mp4 [18.54M]

14 – 3 – regularization and vc theory (08-15).mp4 [7.14M]

14 – 4 – general regularizers (13-28).mp4 [11.24M]

15 – 1 – model selection problem (16-00).mp4 [13.26M]

15 – 2 – validation (13-24).mp4 [10.47M]

15 – 3 – leave-one-out cross validation (16-06).mp4 [12.27M]

15 – 4 – v-fold cross validation (10-41).mp4 [9.17M]

16 – 1 – occam-s razor (10-08).mp4 [8.21M]

16 – 2 – sampling bias (11-50).mp4 [10.26M]

16 – 3 – data snooping (12-28).mp4 [10.80M]

16 – 4 – power of three (08-49).mp4 [7.55M]

2 – 1 – perceptron hypothesis set (15-42).mp4 [18.55M]

2 – 2 – perceptron learning algorithm (pla) (19-46).mp4 [16.61M]

2 – 3 – guarantee of pla (12-37).mp4 [14.45M]

2 – 4 – non-separable data (12-55).mp4 [33.75M]

3 – 1 – learning with different output space (17-26).mp4 [16.16M]

3 – 2 – learning with different data label (18-12).mp4 [50.14M]

3 – 3 – learning with different protocol (11-09).mp4 [31.41M]

3 – 4 – learning with different input space (14-13).mp4 [40.89M]

4 – 1 – learning is impossible- (13-32).mp4 [11.47M]

4 – 2 – probability to the rescue (11-33).mp4 [9.86M]

4 – 3 – connection to learning (16-46).mp4 [14.29M]

4 – 4 – connection to real learning (18-06).mp4 [15.05M]

5 – 1 – recap and preview (13-44).mp4 [11.35M]

5 – 2 – effective number of lines (15-26).mp4 [12.57M]

5 – 3 – effective number of hypotheses (16-17).mp4 [13.12M]

5 – 4 – break point (07-44).mp4 [6.60M]

6 – 1 – restriction of break point (14-18).mp4 [11.52M]

6 – 2 – bounding function- basic cases (06-56).mp4 [5.50M]

6 – 3 – bounding function- inductive cases (14-47).mp4 [11.64M]

6 – 4 – a pictorial proof (16-01).mp4 [12.85M]

7 – 1 – definition of vc dimension (13-10).mp4 [10.67M]

7 – 2 – vc dimension of perceptrons (13-27).mp4 [9.97M]

7 – 3 – physical intuition of vc dimension (6-11).mp4 [5.16M]

7 – 4 – interpreting vc dimension (17-13).mp4 [13.55M]

8 – 1 – noise and probabilistic target (17-01).mp4 [13.93M]

8 – 2 – error measure (15-10).mp4 [11.40M]

8 – 3 – algorithmic error measure (13-46).mp4 [10.98M]

8 – 4 – weighted classification (16-54).mp4 [13.11M]

9 – 1 – linear regression problem (10-08).mp4 [8.04M]

9 – 2 – linear regression algorithm (20-03).mp4 [14.51M]

9 – 3 – generalization issue (20-34).mp4 [15.28M]

9 – 4 – linear regression for binary classification (11-23).mp4 [9.05M]

机器学习技法_国立台湾大学(林轩田) [1.40G]

01_linear_support_vector_machine [84.41M]

01_course_introduction_4-07.mp4 [5.53M]

01_course_introduction_4-07.pdf [1.18M]

02_large-margin_separating_hyperplane_14-17.mp4 [17.54M]

03_standard_large-margin_problem_19-16.mp4 [23.89M]

04_support_vector_machine_15-33.mp4 [19.19M]

05_reasons_behind_large-margin_hyperplane_13-31.mp4 [17.08M]

02_dual_support_vector_machine [76.36M]

01_motivation_of_dual_svm_15-54.mp4 [20.31M]

01_motivation_of_dual_svm_15-54.pdf [578.55K]

02_lagrange_dual_svm_18-50.mp4 [23.45M]

03_solving_dual_svm_14-19.mp4 [17.78M]

04_messages_behind_dual_svm_11-18.mp4 [14.25M]

03_kernel_support_vector_machine [76.32M]

01_kernel_trick_20-23.mp4 [25.20M]

01_kernel_trick_20-23.pdf [1.17M]

02_polynomial_kernel_12-16.mp4 [14.88M]

03_gaussian_kernel_14-43.mp4 [18.17M]

04_comparison_of_kernels_13-35.mp4 [16.89M]

04_soft-margin_support_vector_machine [58.83M]

01_motivation_and_primal_problem_14-27.mp4 [18.20M]

01_motivation_and_primal_problem_14-27.pdf [1.99M]

02_dual_problem_7-38.mp4 [9.19M]

03_messages_behind_soft-margin_svm_13-44.mp4 [16.81M]

04_model_selection_9-57.mp4 [12.63M]

05_kernel_logistic_regression [63.20M]

01_soft-margin_svm_as_regularized_model_13-40.mp4 [17.01M]

01_soft-margin_svm_as_regularized_model_13-40.pdf [560.71K]

02_svm_versus_logistic_regression_10-18.mp4 [12.99M]

03_svm_for_soft_binary_classification_9-36.mp4 [12.23M]

04_kernel_logistic_regression_16-22.mp4 [20.42M]

06_support_vector_regression [72.48M]

01_kernel_ridge_regression_17-17.mp4 [21.43M]

01_kernel_ridge_regression_17-17.pdf [752.20K]

02_support_vector_regression_primal_18-44.mp4 [22.94M]

03_support_vector_regression_dual_13-05.mp4 [15.86M]

04_summary_of_kernel_models_09-06.mp4 [11.51M]

07_blending_and_bagging [89.09M]

01_motivation_of_aggregation_18-54.mp4 [23.88M]

01_motivation_of_aggregation_18-54.pdf [4.28M]

02_uniform_blending_20-31.mp4 [24.95M]

03_linear_and_any_blending_16-48.mp4 [20.95M]

04_bagging_bootstrap_aggregation_11-48.mp4 [15.03M]

08_adaptive_boosting [69.87M]

01_motivation_of_boosting_12-47.mp4 [16.06M]

01_motivation_of_boosting_12-47.pdf [5.24M]

02_diversity_by_re-weighting_14-28.mp4 [17.90M]

03_adaptive_boosting_algorithm_13-34.mp4 [16.72M]

04_adaptive_boosting_in_action_11-04.mp4 [13.94M]

09_decision_tree [61.40M]

01_decision_tree_hypothesis_17-28.mp4 [21.83M]

01_decision_tree_hypothesis_17-28.pdf [740.49K]

02_decision_tree_algorithm_15-20.mp4 [19.03M]

03_decision_tree_heuristics_in_crt_13-21.mp4 [16.81M]

04_decision_tree_in_action_8-44.mp4 [3.00M]

10_random_forest [76.81M]

01_random_forest_algorithm_13-06.mp4 [16.76M]

01_random_forest_algorithm_13-06.pdf [2.48M]

02_out-of-bag_estimate_12-31.mp4 [15.67M]

03_feature_selection_19-27.mp4 [24.40M]

04_random_forest_in_action13-28.mp4 [17.49M]

11_gradient_boosted_decision_tree [92.15M]

01_adaptive_boosted_decision_tree_15-05.mp4 [18.99M]

01_adaptive_boosted_decision_tree_15-05.pdf [2.55M]

02_optimization_view_of_adaboost_27-25.mp4 [33.69M]

03_gradient_boosting_18-20.mp4 [22.37M]

04_summary_of_aggregation_models_11-19.mp4 [14.56M]

12_neural_network [96.24M]

01_motivation_20-36.mp4 [25.49M]

01_motivation_20-36.pdf [1.22M]

02_neural_network_hypothesis_18-01.mp4 [22.73M]

03_neural_network_learning_20-15.mp4 [24.83M]

04_optimization_and_regularization_17-29.mp4 [21.97M]

13_deep_learning [96.72M]

01_deep_neural_network_21-30.mp4 [27.29M]

01_deep_neural_network_21-30.pdf [582.82K]

02_autoencoder_15-17.mp4 [19.51M]

03_denoising_autoencoder_8-30.mp4 [10.77M]

04_principal_component_analysis_31-20.mp4 [38.58M]

14_radial_basis_function_network [294.43M]

01_rbf_network_hypothesis_12-55.mp4 [148.35M]

01_rbf_network_hypothesis_12-55.pdf [949.72K]

02_rbf_network_learning_20-08.mp4 [24.85M]

03_k-means_algorithm_16-19.mp4 [20.47M]

04_k-means_and_rbf_network_in_action_9-46.mp4 [99.84M]

15_matrix_factorization [72.96M]

15 – 1 – linear network hypothesis (20-16).mp4 [25.35M]

15 – 2 – basic matrix factorization (16-32).mp4 [20.21M]

15 – 3 – stochastic gradient descent (12-22).mp4 [15.29M]

15 – 4 – summary of extraction models (9-12).mp4 [11.55M]

215_handout.pdf [573.01K]

16_finale [56.38M]

16 – 1 – feature exploitation techniques (16-11).mp4 [20.75M]

16 – 2 – error optimization techniques (8-40).mp4 [10.67M]

16 – 3 – overfitting elimination techniques (6-44).mp4 [8.20M]

16 – 4 – machine learning in action (12-59).mp4 [16.31M]

216_handout.pdf [458.52K]

炼数成金-机器学习 [4.53G]

第1课 机器学习概论 [785.21M]

ml01.pdf [2.41M]

ml01a.mp4 [178.93M]

ml01b.mp4 [66.37M]

ml01c.mp4 [349.54M]

ml01d.mp4 [187.96M]

第2课 线性回归与logistic。案例：电子商务业绩预测 [658.81M]

ml02.pdf [1.75M]

ml02a.mp4 [82.64M]

ml02b.mp4 [155.45M]

ml02c.mp4 [93.26M]

ml02d.mp4 [80.86M]

ml02e.mp4 [42.29M]

ml02f.mp4 [89.74M]

ml02g.mp4 [18.51M]

ml02h.mp4 [63.45M]

r-modeling.pdf [9.49M]

top_1000_sites.tsv [59.84K]

假设检验讲解.rar [21.25M]

薛毅书源程序.rar [68.84K]

第3课 岭回归，lasso，变量选择技术。案例：凯撒密码破译 [606.72M]

20140408_213926.jpg [1.72M]

20140408_214028.jpg [1.69M]

ml03.pdf [2.00M]

ml03a.mp4 [127.95M]

ml03b.mp4 [91.48M]

ml03c.mp4 [123.54M]

ml03d.mp4 [67.95M]

ml03e.mp4 [116.66M]

ml03f.mp4 [73.74M]

资料 [66.25M]

dm_practical_ml_tools_and_techs.rar [5.11M]

mit.foundations of ml.rar [2.82M]

mit.introduction to ml.2ed.rar [3.17M]

ml.part1.rar [9.99M]

ml.part2.rar [9.99M]

ml.part3.rar [2.55M]

数据挖掘中文第三版.part1.rar [9.99M]

数据挖掘中文第三版.part2.rar [9.99M]

数据挖掘中文第三版.part3.rar [9.99M]

数据挖掘中文第三版.part4.rar [2.65M]

机器学习第10周.rar [323.21M]

机器学习第11周.rar [361.67M]

机器学习第4周.rar [297.31M]

机器学习第5周.rar [223.17M]

机器学习第6周.rar [209.76M]

机器学习第7周.rar [338.60M]

机器学习第8周.rar [369.85M]

机器学习第9周.rar [393.67M]

解压密码.txt [0.05K]

龙星计划_机器学 [4.24G]

lecture01（更多视频资料关注）.mp4 [239.28M]

lecture02（更多视频资料关注）.mp4 [222.03M]

lecture03（更多视频资料关注）.mp4 [243.43M]

lecture04（更多视频资料关注）.mp4 [255.50M]

lecture05（更多视频资料关注）.mp4 [232.25M]

lecture06（更多视频资料关注）.mp4 [135.64M]

lecture07（更多视频资料关注）.mp4 [252.20M]

lecture08（更多视频资料关注）.mp4 [209.73M]

lecture09（更多视频资料关注）.mp4 [227.57M]

lecture10（更多视频资料关注）.mp4 [281.58M]

lecture11（更多视频资料关注）.mp4 [207.82M]

lecture12（更多视频资料关注）.mp4 [237.86M]

lecture13（更多视频资料关注）.mp4 [249.06M]

lecture14（更多视频资料关注）.mp4 [213.10M]

lecture15（更多视频资料关注）.mp4 [221.45M]

lecture16（更多视频资料关注）.mp4 [248.63M]

lecture17（更多视频资料关注）.mp4 [201.45M]

lecture18（更多视频资料关注）.mp4 [220.02M]

lecture19_r（更多视频资料关注）.mp4 [247.59M]

下载之前必看！更多视频资料下载目录.docx [479.29K]

模式识别_35_国防科学技术大学(蔡宣平) [2.66G]

01.概述.flv [78.64M]

02.特征矢量及特征空间、随机矢量、正态分布特性.flv [80.52M]

03.聚类分析的概念、相似性测度.flv [83.17M]

04.相似性测度（二）.flv [85.84M]

05.类间距离、准则函数.flv [75.86M]

06.聚类算法：简单聚类算法、谱系聚类算法.flv [87.05M]

07.聚类算法：动态聚类算法——c均值聚类算法.flv [66.62M]

08.聚类算法：动态聚类算法——近邻函数算法.flv [88.00M]

09.聚类算法实验.flv [12.93M]

10.判别域界面方程分类的概念、线性判别函数.flv [66.89M]

11.判别函数值的鉴别意义、权空间及解空间、fisher线性判别.flv [94.27M]

12.线性可分条件下判别函数权矢量算法.flv [95.82M]

13.一般情况下的判别函数权矢量算法.flv [75.44M]

14.非线性判别函数.flv [110.17M]

15.最近邻方法.flv [81.42M]

16.感知器算法实验.flv [11.15M]

17.最小误判概率准则.flv [78.78M]

18.正态分布的最小误判概率、最小损失准则判决.flv [95.78M]

19.含拒绝判决的最小损失准则、最小最大损失准则.flv [86.22M]

20.neyman—pearson判决、实例.flv [73.79M]

21.概述、矩法估计、最大似然估计.flv [80.00M]

22.贝叶斯估计.flv [74.45M]

23.贝叶斯学习.flv [91.37M]

24.概密的窗函数估计方法.flv [106.70M]

25.有限项正交函数级数逼近法.flv [83.89M]

26.错误率估计.flv [62.26M]

27.小结.flv [73.23M]

28.实验3-4-5 bayes分类器-knn分类器-视频动目标检测.flv [72.58M]

29.概述、类别可分性判据（一）.flv [90.60M]

30.类别可分性判据（二）.flv [89.36M]

31.基于可分性判据的特征提取.flv [99.45M]

32.离散kl变换与特征提取.flv [66.85M]

33.离散kl变换在特征提取与选择中的应用.flv [66.84M]

34.特征选择中的直接挑选法.flv [57.54M]

35.综合实验-图像中的字符识别.flv [84.92M]

统计机器学习_41_上海交大(张志华) [7.77G]

01 概率基础.mp4 [224.96M]

02 随机变量1.mp4 [222.51M]

03 随机变量2.mp4 [233.79M]

04 高斯分布.mp4 [218.95M]

05 高斯分布例子.mp4 [224.05M]

06 连续分布.mp4 [205.03M]

07 jeffrey prior.mp4 [213.12M]

08 scale mixture pisribarin.mp4 [371.55M]

09 statistic interence.mp4 [188.20M]

10 laplace 变换.mp4 [237.59M]

11 多元分布定义.mp4 [185.37M]

12 概率变换.mp4 [180.62M]

13 jacobian.mp4 [178.49M]

14 wedge production.mp4 [180.58M]

15 wishart 分布.mp4 [202.04M]

16 多元正态分布.mp4 [202.97M]

17 统计量.mp4 [197.62M]

18 矩阵元beta分布.mp4 [76.21M]

19 共轭先验性质.mp4 [111.30M]

20 统计量 充分统计量.mp4 [210.65M]

21 指数值分布.mp4 [195.03M]

22 entropy.mp4 [223.79M]

23 kl distance.mp4 [198.10M]

24 properties.mp4 [125.99M]

25 概率不等式1.mp4 [225.13M]

26 概率不等式2.mp4 [188.98M]

27 概率不等式1.mp4 [206.29M]

28 概率不等式2.mp4 [183.60M]

29 概率不等式3.mp4 [187.71M]

30 john 引理.mp4 [145.53M]

31 概率不等式.mp4 [200.86M]

32 随机投影.mp4 [195.56M]

33 stochastic convergence-概念.mp4 [225.51M]

34 stochastic convergence-性质.mp4 [146.08M]

35 stochastic convergence-应用.mp4 [125.94M]

36 em算法1.mp4 [229.42M]

37 em算法2.mp4 [206.49M]

38 em算法3.mp4 [142.07M]

39 bayesian classification.mp4 [201.56M]

40 markov chain monte carlo1.mp4 [232.90M]

41 markov chain monte carlo2.mp4 [104.90M]

南京大学周志华老师的一个讲普适机器学习的ppt精品-ppt.ppt [939.50K]