七月在线机器学习

七月在线机器学习
ML_3月机器学习在线班
material
4月19日晚的分享_黄高乐
4月19日晚的分享_黄高乐
单纯形法源代码_by C
Debug
simplex.exe 216.10kb
simplex.ilk 234.45kb
simplex.obj 31.25kb
simplex.pch 2.02M
simplex.pdb 473.00kb
vc60.idb 97.00kb
vc60.pdb 76.00kb
amoeba.c 1.61kb
amotry.c 0.53kb
nrutil.c 8.21kb
nrutil.h 3.25kb
simplex.cpp 0.87kb
simplex.dsp 3.33kb
simplex.dsw 0.53kb
simplex.ncb 49.00kb
simplex.opt 47.50kb
simplex.plg 1.17kb
4月19日学员分享.pptx 878.41kb
1.1微积分与概率论.pdf 1.55M
1.微积分与概率论.pdf 1.54M
10.1贝叶斯网络.pdf 3.55M
11.支持向量机.pdf 1.80M
12.EM和GMM.pdf 749.44kb
13.0主题模型_预习材料.pdf 1.19M
13.主题模型.pdf 1.62M
14.隐马尔科夫模型.pdf 743.36kb
2.1.1参数估计的评价准则.pdf 148.36kb
2.1参数估计与矩阵运算.pdf 934.57kb
2.参数估计与矩阵运算.pdf 919.05kb
2012.李航.统计学习方法.pdf 17.56M
3.凸优化.pdf 3.43M
4.1广义线性回归和对偶优化.pdf 3.45M
5.梯度下降和拟牛顿.pdf 1.34M
6.最大熵模型.pdf 753.13kb
7.聚类.pdf 3.31M
8.决策树与随机森林.pdf 2.68M
9.Adaboost导论.pdf 298.75kb
9.贝叶斯网络.ppt 7.52M
Adaboost.pdf 1.03M
Adaboost.py 4.76kb
book11April2014.pdf 1.87M
CART.py 6.13kb
Finding scientific topics.pdf 704.14kb
kernel.py 1.34kb
lda.py 7.37kb
mcmc.pdf 1.50M
七月教育LDA学员分享_version2.pdf 359.15kb
凸优化-中译本(扫描).pdf 49.56M
推荐系统实践.pdf 12.39M
学习率代码.cpp 1.43kb
video
01 微积分与概率论基础
01 微积分与概率论基础.mp4 1.45G
七月算法 概率论.flv 221.95M
七月算法 概率面试题精讲.flv 159.27M
七月算法 数理统计.flv 254.51M
02 参数估计与矩阵运算基础
02 参数估计与矩阵运算基础.mp4 1.50G
七月算法 矩阵运算修改版.flv 204.50M
03 凸优化基础
03 凸优化基础.mp4 1.48G
七月算法 凸优化.flv 131.43M
04 广义线性回归和对偶优化
04 广义线性回归和对偶优化.mp4 1.85G
05 牛顿、拟牛顿、梯度下降、随机梯度下降(SGD)
05 梯度下降和拟牛顿.mp4 1.60G
06 熵、最大熵模型MaxEnt、改进的迭代尺度法IIS
06 最大熵模型.mp4 1.61G
07 聚类（k-means、层次聚类、谱聚类等）
07 聚类方法.mp4 1.60G
七月算法 Kmeans聚类.flv 215.71M
七月算法 谱聚类.flv 215.66M
08 K近邻、决策树、随机森林(random decision forests)
08 决策树和随机森林.mp4 1.60G
09 Adaboost
09 Adaboost.mp4 1.30G
七月算法 Adaboost.flv 148.32M
10 朴素贝叶斯、与贝叶斯网络
10 贝叶斯网络.mp4 1.79G
七月算法 贝叶斯网络.flv 189.30M
七月算法 贝叶斯网络节选.flv 307.27M
11 支持向量机（最大间隔分类、拉格朗日乘值、对偶问题、损失函数、最优化理论、SMO）
11 支持向量机.mp4 1.49G
七月算法 SVM数据试验.flv 136.26M
七月算法 支持向量机(上).flv 219.62M
七月算法 支持向量机(下).flv 164.38M
七月算法 支持向量机(中).flv 198.01M
12 EM、混合高斯模型
12 EM.mp4 938.90M
七月算法 18分钟理解EM算法.mp4 33.85M
七月算法 EM.flv 204.81M
七月算法 感性理解EM算法-GMM.mp4 65.82M
12 衣服推荐系统
12 衣服推荐系统by黄高乐.mp4 493.45M
13 主题模型（概率潜语义分析PLSA、隐含狄利克雷分布LDA）
13 主题模型.mp4 2.31G
七月算法 主题模型(上).flv 324.87M
七月算法 主题模型(下).flv 104.28M
七月算法 主题模型(中).flv 422.51M
14.15 马尔科夫链、隐马尔可夫模型HMM、采样
15 IP与MCMC(上).mp4 509.71M
15 IP与MCMC(下).mp4 544.96M
15 IP与MCMC(中).mp4 533.19M
16 马尔可夫随机场(Markov Random Field)、条件随机场CRF
七月算法 条件随机场(上).flv 337.96M
七月算法 条件随机场(下).flv 306.20M
七月算法 条件随机场(中).flv 334.79M
17 SVD、主成分分析PCA、因子分析、独立成分分析ICA
17 PCA-SVD(上).mp4 520.14M
17 PCA-SVD(下).mp4 424.62M
17 PCA-SVD(中).mp4 498.12M
18 卷积神经网络(CNN)、深度学习浅析
18 CNN.mp4 1.78G
19 变分推断方法
20 知识图谱
20 代码实现.mp4 1.23G
ML_9月机器学习在线班
8_9_随机森林_SVM
css
fonts
glyphicons-halflings-regular.eot 19.81kb
glyphicons-halflings-regular.svg 61.38kb
glyphicons-halflings-regular.ttf 40.27kb
glyphicons-halflings-regular.woff 22.75kb
glyphicons-halflings-regular.woff2 17.61kb
lib
bootstrap-theme.min.css 22.81kb
bootstrap.min.css 119.67kb
jquery-ui.min.css 29.46kb
jquery-ui.structure.min.css 14.42kb
jquery-ui.theme.min.css 13.54kb
mystyle.css 0.07kb
data
titanic
makedb.js 2.16kb
submit.csv 3.18kb
test.csv 27.96kb
testData.js 65.98kb
train.csv 59.76kb
trainData.js 152.92kb
images
2dground.png 34.43kb
smo_alpha_pair.png 40.76kb
smo_b_update.png 92.92kb
js
lib
ace
snippets
abap.js 0.17kb
abc.js 1.07kb
actionscript.js 3.27kb
ada.js 0.17kb
apache_conf.js 0.18kb
applescript.js 0.18kb
asciidoc.js 0.18kb
assembly_x86.js 0.18kb
autohotkey.js 0.18kb
batchfile.js 0.18kb
c9search.js 0.18kb
cirru.js 0.17kb
clojure.js 2.22kb
cobol.js 0.17kb
coffee.js 2.41kb
coldfusion.js 0.18kb
csharp.js 0.17kb
css.js 21.12kb
curly.js 0.17kb
c_cpp.js 2.90kb
d.js 0.16kb
dart.js 1.49kb
diff.js 0.60kb
django.js 4.16kb
dockerfile.js 0.18kb
dot.js 0.17kb
eiffel.js 0.17kb
ejs.js 0.17kb
elixir.js 0.17kb
elm.js 0.17kb
erlang.js 3.84kb
forth.js 0.17kb
ftl.js 0.17kb
gcode.js 0.17kb
gherkin.js 0.17kb
gitignore.js 0.18kb
glsl.js 0.17kb
golang.js 0.17kb
groovy.js 0.17kb
haml.js 0.52kb
handlebars.js 0.18kb
haskell.js 2.12kb
haxe.js 0.17kb
html.js 20.17kb
html_ruby.js 0.18kb
ini.js 0.17kb
io.js 1.83kb
jack.js 0.17kb
jade.js 0.17kb
java.js 4.76kb
javascript.js 4.17kb
json.js 0.17kb
jsoniq.js 1.84kb
jsp.js 3.04kb
jsx.js 0.17kb
julia.js 0.17kb
latex.js 0.17kb
lean.js 0.17kb
less.js 0.17kb
liquid.js 0.17kb
lisp.js 0.17kb
livescript.js 0.18kb
live_script.js 0.17kb
logiql.js 0.17kb
lsl.js 37.04kb
lua.js 0.58kb
luapage.js 0.17kb
lucene.js 0.17kb
makefile.js 0.24kb
markdown.js 2.16kb
mask.js 0.17kb
matlab.js 0.17kb
maze.js 0.32kb
mel.js 0.17kb
mipsassembler.js 0.17kb
mips_assembler.js 0.19kb
mushcode.js 0.18kb
mysql.js 0.17kb
nix.js 0.17kb
objectivec.js 0.18kb
ocaml.js 0.17kb
pascal.js 0.17kb
perl.js 6.10kb
pgsql.js 0.17kb
php.js 7.38kb
plain_text.js 0.18kb
powershell.js 0.18kb
praat.js 0.17kb
prolog.js 0.17kb
properties.js 0.18kb
protobuf.js 0.17kb
python.js 3.95kb
r.js 2.85kb
rdoc.js 0.17kb
rhtml.js 0.17kb
ruby.js 22.72kb
rust.js 0.17kb
sass.js 0.17kb
scad.js 0.17kb
scala.js 0.17kb
scheme.js 0.17kb
scss.js 0.17kb
sh.js 2.22kb
sjs.js 0.17kb
smarty.js 0.17kb
snippets.js 0.35kb
soy_template.js 0.18kb
space.js 0.17kb
sql.js 1.01kb
sqlserver.js 2.26kb
stylus.js 0.17kb
svg.js 0.17kb
swift.js 0.17kb
swig.js 0.17kb
tcl.js 1.87kb
tex.js 3.96kb
text.js 0.17kb
textile.js 0.63kb
toml.js 0.17kb
twig.js 0.17kb
typescript.js 0.18kb
vala.js 4.90kb
vbscript.js 0.18kb
velocity.js 0.74kb
verilog.js 0.17kb
vhdl.js 0.17kb
xml.js 0.17kb
xquery.js 1.84kb
yaml.js 0.17kb
ace.js 630.40kb
ext-beautify.js 8.08kb
ext-chromevox.js 13.29kb
ext-elastic_tabstops_lite.js 8.55kb
ext-emmet.js 41.87kb
ext-error_marker.js 0.14kb
ext-keybinding_menu.js 5.71kb
ext-language_tools.js 65.71kb
ext-linking.js 1.42kb
ext-modelist.js 5.53kb
ext-old_ie.js 15.58kb
ext-searchbox.js 13.07kb
ext-settings_menu.js 20.03kb
ext-spellcheck.js 2.35kb
ext-split.js 7.22kb
ext-static_highlight.js 4.91kb
ext-statusbar.js 1.57kb
ext-textarea.js 18.24kb
ext-themelist.js 2.30kb
ext-whitespace.js 5.31kb
keybinding-emacs.js 39.47kb
keybinding-vim.js 199.65kb
mode-abap.js 9.40kb
mode-abc.js 8.79kb
mode-actionscript.js 24.00kb
mode-ada.js 2.71kb
mode-apache_conf.js 18.56kb
mode-applescript.js 9.35kb
mode-asciidoc.js 13.46kb
mode-assembly_x86.js 11.38kb
mode-autohotkey.js 65.61kb
mode-batchfile.js 7.99kb
mode-c9search.js 9.03kb
mode-cirru.js 5.79kb
mode-clojure.js 12.44kb
mode-cobol.js 3.29kb
mode-coffee.js 14.71kb
mode-coldfusion.js 101.24kb
mode-csharp.js 30.31kb
mode-css.js 35.73kb
mode-curly.js 99.44kb
mode-c_cpp.js 32.69kb
mode-d.js 17.05kb
mode-dart.js 38.54kb
mode-diff.js 4.35kb
mode-django.js 100.26kb
mode-dockerfile.js 28.83kb
mode-dot.js 13.56kb
mode-eiffel.js 4.83kb
mode-ejs.js 116.83kb
mode-elixir.js 22.70kb
mode-elm.js 9.06kb
mode-erlang.js 45.25kb
mode-forth.js 10.92kb
mode-ftl.js 49.07kb
mode-gcode.js 2.72kb
mode-gherkin.js 4.54kb
mode-gitignore.js 1.40kb
mode-glsl.js 35.73kb
mode-golang.js 27.38kb
mode-groovy.js 52.72kb
mode-haml.js 18.68kb
mode-handlebars.js 100.94kb
mode-haskell.js 16.68kb
mode-haxe.js 26.53kb
mode-html.js 97.79kb
mode-html_ruby.js 117.59kb
mode-ini.js 4.52kb
mode-io.js 9.18kb
mode-jack.js 25.23kb
mode-jade.js 90.20kb
mode-java.js 51.28kb
mode-javascript.js 45.67kb
mode-json.js 24.17kb
mode-jsoniq.js 333.57kb
mode-jsp.js 70.81kb
mode-jsx.js 27.62kb
mode-julia.js 11.56kb
mode-latex.js 7.09kb
mode-lean.js 9.44kb
mode-less.js 35.58kb
mode-liquid.js 48.68kb
mode-lisp.js 3.10kb
mode-livescript.js 8.43kb
mode-live_script.js 26.69kb
mode-logiql.js 24.88kb
mode-lsl.js 45.35kb
mode-lua.js 14.15kb
mode-luapage.js 113.85kb
mode-lucene.js 2.02kb
mode-makefile.js 11.27kb
mode-markdown.js 110.36kb
mode-mask.js 83.43kb
mode-matlab.js 22.61kb
mode-maze.js 8.79kb
mode-mel.js 42.69kb
mode-mipsassembler.js 6.07kb
mode-mips_assembler.js 9.32kb
mode-mushcode.js 11.71kb
mode-mysql.js 8.48kb
mode-nix.js 37.42kb
mode-objectivec.js 63.58kb
mode-ocaml.js 20.95kb
mode-pascal.js 7.37kb
mode-perl.js 12.72kb
mode-pgsql.js 78.69kb
mode-php.js 224.55kb
mode-plain_text.js 0.74kb
mode-powershell.js 29.26kb
mode-praat.js 17.04kb
mode-prolog.js 13.70kb
mode-properties.js 1.96kb
mode-protobuf.js 35.91kb
mode-python.js 8.47kb
mode-r.js 9.34kb
mode-rdoc.js 6.63kb
mode-rhtml.js 107.01kb
mode-ruby.js 32.84kb
mode-rust.js 11.10kb
mode-sass.js 16.69kb
mode-scad.js 27.21kb
mode-scala.js 52.91kb
mode-scheme.js 6.77kb
mode-scss.js 36.42kb
mode-sh.js 27.22kb
mode-sjs.js 51.64kb
mode-smarty.js 102.74kb
mode-snippets.js 6.57kb
mode-soy_template.js 111.04kb
mode-space.js 4.63kb
mode-sql.js 2.92kb
mode-sqlserver.js 22.75kb
mode-stylus.js 19.04kb
mode-svg.js 72.00kb
mode-swift.js 27.38kb
mode-swig.js 47.05kb
mode-tcl.js 11.79kb
mode-tex.js 4.67kb
mode-text.js 0.00kb
mode-textile.js 3.81kb
mode-toml.js 3.81kb
mode-twig.js 103.45kb
mode-typescript.js 48.75kb
mode-vala.js 41.29kb
mode-vbscript.js 7.97kb
mode-velocity.js 106.90kb
mode-verilog.js 3.79kb
mode-vhdl.js 3.50kb
mode-xml.js 22.86kb
mode-xquery.js 330.84kb
mode-yaml.js 7.68kb
theme-ambiance.js 27.53kb
theme-chaos.js 3.13kb
theme-chrome.js 2.96kb
theme-clouds.js 2.28kb
theme-clouds_midnight.js 2.62kb
theme-cobalt.js 2.58kb
theme-crimson_editor.js 3.04kb
theme-dawn.js 2.46kb
theme-dreamweaver.js 3.40kb
theme-eclipse.js 2.33kb
theme-github.js 2.40kb
theme-idle_fingers.js 2.44kb
theme-iplastic.js 6.62kb
theme-katzenmilch.js 3.35kb
theme-kr_theme.js 2.50kb
theme-kuroir.js 2.19kb
theme-merbivore.js 2.43kb
theme-merbivore_soft.js 2.64kb
theme-monokai.js 2.58kb
theme-mono_industrial.js 2.99kb
theme-pastel_on_dark.js 2.84kb
theme-solarized_dark.js 2.49kb
theme-solarized_light.js 2.54kb
theme-sqlserver.js 3.16kb
theme-terminal.js 3.13kb
theme-textmate.js 2.84kb
theme-tomorrow.js 2.77kb
theme-tomorrow_night.js 3.02kb
theme-tomorrow_night_blue.js 3.23kb
theme-tomorrow_night_bright.js 3.71kb
theme-tomorrow_night_eighties.js 3.42kb
theme-twilight.js 2.71kb
theme-vibrant_ink.js 2.40kb
theme-xcode.js 2.11kb
worker-coffee.js 220.34kb
worker-css.js 290.01kb
worker-html.js 330.28kb
worker-javascript.js 337.08kb
worker-json.js 71.00kb
worker-lua.js 103.31kb
worker-php.js 230.80kb
worker-xml.js 110.14kb
worker-xquery.js 2.67M
bootstrap.min.js 35.95kb
d3.min.js 147.60kb
jquery-1.11.3.min.js 93.71kb
jquery-ui-1.11.4.min.js 234.79kb
remarkable.js 252.98kb
index.js 1.88kb
practice_logistic.html 10.17kb
practice_rf.html 14.09kb
practice_svm.html 21.66kb
rf.pdf 1.01M
svm.pdf 2.28M
回归代码
d8.txt 3.67kb
Regression.py 4.27kb
基础补习-概率-台湾大学叶柄成
第八周
9 – 1 – 8-0：咱們聊聊，如何探索有意義的人生？.mp4 191.65M
9 – 2 – 8-1.a：聯合機率分佈 (上) (14-36).mp4 48.10M
9 – 3 – 8-1.b：聯合機率分佈 (中) (15-05).mp4 51.07M
9 – 4 – 8-1.c：聯合機率分佈 (下) (17-00).mp4 60.54M
9 – 5 – 8-1.d：聯合機率分佈 (末) (11-18).mp4 18.16M
9 – 6 – 8-2：邊際機率分佈 (12-32).mp4 19.28M
9 – 7 – 8-3.a：雙變數期望值 (上) (10-06)_2.mp4 35.73M
9 – 8 – 8-3.b：雙變數期望值 (下) (17-27).mp4 64.18M
第二周
2 – 3 – 1-2.a：集合論 (上) (11-46).mp4 16.78M
2 – 4 – 1-2.b：集合論 (下) (09-40).mp4 14.02M
2 – 5 – 1-3.a：機率名詞 (上) (11-24).mp4 17.37M
2 – 6 – 1-3.b：機率名詞 (下) (16-36).mp4 25.14M
第九周
10 – 1 – 9-1.a：隨機變數之和 (上) (11-18).mp4 46.16M
10 – 2 – 9-1.b：隨機變數之和 (下) (13-49).mp4 23.35M
10 – 3 – 9-2.a：MGF (上) (10-17).mp4 41.43M
10 – 4 – 9-2.b：MGF (中) (14-06)_2.mp4 56.76M
10 – 5 – 9-2.c：MGF (下) (15-53).mp4 25.69M
10 – 6 – 9-3.a：多個隨機變數和 (上) (10-35).mp4 43.30M
10 – 7 – 9-3.b：多個隨機變數和 (下) (13-01).mp4 54.16M
10 – 8 – 9-4.a：中央極限定理-萬佛朝宗 (上) (16-45).mp4 28.13M
10 – 9 – 9-4.b：中央極限定理-萬佛朝宗 (下) (17-19).mp4 73.48M
第六周
7 – 1 – 6-0：咱們聊聊，成功者的條件是什麼？ (10-13).mp4 42.78M
7 – 2 – 6-1.a：連續機率分佈 II (上) (15-25)(1).mp4 26.38M
7 – 2 – 6-1.a：連續機率分佈 II (上) (15-25).mp4 26.38M
7 – 3 – 6-1.b：連續機率分佈 II (中) (16-08).mp4 28.14M
7 – 4 – 6-1.c：連續機率分佈 II (下) (17-16).mp4 75.12M
7 – 5 – 6-1.d：連續機率分佈 II (末) (5-40).mp4 9.38M
7 – 6 – 6-2.a：期望值 I (上) (16-35).mp4 27.76M
7 – 7 – 6-2.b：期望值 I (中) (10-41).mp4 17.57M
7 – 8 – 6-2.c：期望值 I (下) (16-44).mp4 27.89M
7 – 9 – 6-2.d：期望值 I (末) (14-30).mp4 25.09M
第七周
8 – 1 – 7-0：咱們聊聊，每天都在忙，忙的有用嗎？.mp4 105.29M
8 – 2 – 7-1.a：期望值 II (上) (14-31).mp4 24.23M
8 – 3 – 7-1.b：期望值 II (下) (13-07).mp4 22.06M
8 – 4 – 7-2.a- 隨機變數之函數 (上) (10-35).mp4 17.56M
8 – 5 – 7-2.b- 隨機變數之函數 (下) (08-42).mp4 14.27M
8 – 6 – 7-3.a- 條件機率分佈與失憶性 (上) (15-07).mp4 25.74M
8 – 7 – 7-3b- 條件機率分佈與失憶性 (下) (19-20).mp4 33.60M
第三周
3 – 1 – 2-0：咱們聊聊，是學習，還是應付- (15-32).mp4 62.04M
3 – 1 – 2-0：咱們聊聊，是學習，還是應付- (15-32).zip 61.61M
4 – 2 – 3-1.a：機率的獨立性 (上) (09-12).mp4 13.69M
4 – 3 – 3-1.b：機率的獨立性 (下) (10-35).mp4 16.43M
4 – 4 – 3-2：圖解繁複機率 (08-47).mp4 13.62M
4 – 5 – 3-3.a：數數算機率 (上) (16-57).mp4 24.72M
4 – 6 – 3-3.b：數數算機率 (下) (12-58).mp4 19.50M
第四周
5 – 1 – 4-0：咱們聊聊，如何幫自己面對未來挑戰？ (17-33).mp4 68.92M
5 – 10 – 4-4.b：離散機率分佈 I (下) (8-47).mp4 14.56M
5 – 2 – 4-1.a：隨機變數 (上) (13-53).mp4 22.71M
5 – 3 – 4-1.b：隨機變數 (中) (14-43).mp4 25.11M
5 – 4 – 4-1.c：隨機變數 (下) (5-18).mp4 9.41M
5 – 5 – 4-2.a：累績分佈函數 CDF (上) (9-48).mp4 15.46M
5 – 6 – 4-2.b：累績分佈函數 CDF (中) (8-59).mp4 14.02M
5 – 7 – 4-2.c：累績分佈函數 CDF (下) (9-00).mp4 14.09M
5 – 8 – 4-3：機率質量函數 PMF (11-26).mp4 17.52M
5 – 9 – 4-4.a：離散機率分佈 I (上) (14-41).mp4 23.55M
第五周
6 – 1 – 5-0：咱們聊聊，願你夜夜好眠到天明！ (14-09).mp4 45.70M
6 – 2 – 5-1.a：離散機率分佈 II (上) (10-36).mp4 17.66M
6 – 3 – 5-1.b：離散機率分佈 II (中) (12-06).mp4 20.53M
6 – 4 – 5-1.c：離散機率分佈 II (下) (20-28).mp4 34.47M
6 – 5 – 5-2：機率密度函數 PDF (18-56).mp4 30.03M
6 – 6 – 5-3：連續機率分佈 I (18-12).mp4 28.18M
课堂讲义
Benson_Coursera_Week_1_繁.pdf 2.08M
Benson_Coursera_Week_1_繁空.pdf 1.90M
Benson_Coursera_Week_2_繁.pdf 1.36M
Benson_Coursera_Week_2_繁空.pdf 1.28M
Benson_Coursera_Week_3_繁.pdf 2.54M
Benson_Coursera_Week_3_繁空.pdf 2.01M
Benson_Coursera_Week_4_繁.pdf 2.54M
Benson_Coursera_Week_4_繁空.pdf 2.12M
Benson_Coursera_Week_5_繁.pdf 2.17M
Benson_Coursera_Week_5_繁空.pdf 2.05M
Benson_Coursera_Week_6_繁.pdf 3.15M
Benson_Coursera_Week_6_繁空.pdf 2.32M
Benson_Coursera_Week_7_繁.pdf 3.25M
Benson_Coursera_Week_7_繁空.pdf 1.70M
Benson_Coursera_Week_8_繁.pdf 4.54M
Benson_Coursera_Week_8_繁空.pdf 2.05M
Benson_Coursera_Week_9_繁.pdf 3.07M
Benson_Coursera_Week_9_繁空.pdf 3.52M
课程ppt
1.1微积分与概率论.pdf 3.92M
1.微积分与概率论原.pdf 3.66M
10.降维.pdf 1.29M
11.聚类.pdf 5.43M
12.提升.pdf 4.00M
13.贝叶斯网络.pdf 3.62M
14.EM.pdf 1.94M
15.主题模型.pdf 2.24M
16.采样_更新.pdf 4.57M
17.HMM.pdf 2.37M
18.条件随机场.pdf 1.52M
19_20_神经网络.pdf 6.31M
2.1数理统计与参数估计.pdf 1.09M
3.1矩阵运算.pdf 2.17M
4.凸优化.pdf 3.62M
5.1回归.pdf 3.01M
6.1梯度下降和拟牛顿.pdf 2.89M
7.1最大熵模型.pdf 1.46M
8.1rf.pdf 1.01M
9.1svm.pdf 2.28M
cs229-notes1.pdf 228.34kb
探秘2016校招笔试面试.pdf 2.54M
凸优化_CN.pdf 5.96M
凸优化_EN.pdf 5.52M
0.烟雨蒙蒙.mp4 980.37M
1.微积分和概率论.mp4 2.10G
10.降维.mp4 1.91G
11.聚类.mp4 2.10G
12.Boosting.mp4 1.82G
13.贝叶斯网络.mp4 2.17G
14.EM算法.mp4 889.64M
14.EM算法重制完整版.mp4 2.25G
15.主题模型.mp4 2.51G
16.采样.mp4 3.19G
17.HMM.mp4 224.11M
18.条件随机场.mp4 311.55M
19.人工神经网络.mp4 208.49M
2.数理统计与参数估计.mp4 1.92G
20.CNN&RNN.mp4 232.28M
3.矩阵运算.mp4 1.88G
4.凸优化.mp4 1.87G
5.回归.mp4 1.63G
6.梯度下降和拟牛顿.mp4 1.40G
7.最大熵模型.mp4 1.78G
8.随机森林.mp4 1.62G
9.支持向量机.mp4 1.48G
ML_机器学习其他资料
2014斯坦福大学机器学习mkv视频
pdf
Lecture1.pdf 3.30M
Lecture10.pdf 1.48M
Lecture11.pdf 497.64kb
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 242.37kb
Lecture6.pdf 2.12M
Lecture7.pdf 2.34M
Lecture8.pdf 4.97M
Lecture9.pdf 3.37M
ppt
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 407.28kb
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
mlclass-ex1-jin
computeCost.m 0.68kb
computeCostMulti.m 0.69kb
ex1.m 3.36kb
ex1data1.txt 1.33kb
ex1data2.txt 0.64kb
ex1_multi.m 4.38kb
featureNormalize.m 1.44kb
gradientDescent.m 1.14kb
gradientDescentMulti.m 0.96kb
ml_login_data.mat 0.26kb
normalEqn.m 0.66kb
OGLdpf.log
plotData.m 1.03kb
submit.m 15.22kb
submitWeb.m 10.47kb
warmUpExercise.m 0.51kb
mlclass-ex2-jin
costFunction.m 1.02kb
costFunctionReg.m 1.14kb
ex2.m 3.65kb
ex2.pdf 188.26kb
ex2data1.txt 3.69kb
ex2data2.txt 2.18kb
ex2_reg.m 2.90kb
mapFeature.m 0.50kb
plotData.m 0.98kb
plotDecisionBoundary.m 1.42kb
predict.m 0.82kb
sigmoid.m 0.44kb
submit.m 16.69kb
submitWeb.m 0.79kb
mlclass-ex3-jin
displayData.m 1.47kb
ex3.m 2.09kb
ex3.pdf 327.73kb
ex3data1.mat 7.16M
ex3weights.mat 77.73kb
ex3_nn.m 2.58kb
fmincg.m 8.54kb
lrCostFunction.m 1.90kb
oneVsAll.m 2.16kb
predict.m 1.26kb
predictOneVsAll.m 1.53kb
sigmoid.m 0.13kb
submit.m 16.64kb
submitWeb.m 0.79kb
mlclass-ex4-jin
checkNNGradients.m 1.90kb
computeNumericalGradient.m 1.07kb
debugInitializeWeights.m 0.82kb
displayData.m 1.47kb
ex4.m 7.88kb
ex4.pdf 406.78kb
ex4data1.mat 7.16M
ex4weights.mat 77.73kb
fmincg.m 8.54kb
nnCostFunction.m 5.32kb
predict.m 0.57kb
randInitializeWeights.m 0.96kb
sigmoid.m 0.13kb
sigmoidGradient.m 0.70kb
submit.m 16.73kb
submitWeb.m 0.79kb
mlclass-ex5-jin
ex5.m 7.18kb
ex5.pdf 181.55kb
ex5data1.mat 1.29kb
featureNormalize.m 0.50kb
fmincg.m 8.54kb
learningCurve.m 2.53kb
linearRegCostFunction.m 1.11kb
plotFit.m 0.79kb
polyFeatures.m 0.69kb
submit.m 16.81kb
submitWeb.m 0.79kb
trainLinearReg.m 0.70kb
validationCurve.m 1.96kb
mlclass-ex6-jin
dataset3Params.m 1.92kb
emailFeatures.m 2.07kb
emailSample1.txt 0.38kb
emailSample2.txt 1.27kb
ex6.m 4.03kb
ex6.pdf 355.43kb
ex6data1.mat 0.96kb
ex6data2.mat 7.43kb
ex6data3.mat 5.90kb
ex6_spam.m 4.49kb
gaussianKernel.m 0.71kb
getVocabList.m 0.74kb
linearKernel.m 0.32kb
plotData.m 0.56kb
porterStemmer.m 9.67kb
processEmail.m 3.87kb
readFile.m 0.39kb
spamSample1.txt 0.64kb
spamSample2.txt 0.24kb
spamTest.mat 110.08kb
spamTrain.mat 418.76kb
submit.m 16.44kb
submitWeb.m 0.79kb
svmPredict.m 1.63kb
svmTrain.m 5.82kb
visualizeBoundary.m 0.72kb
visualizeBoundaryLinear.m 0.40kb
vocab.txt 19.77kb
mlclass-ex7-jin
bird_small.mat 44.54kb
bird_small.png 32.26kb
computeCentroids.m 1.25kb
displayData.m 1.47kb
drawLine.m 0.23kb
ex7.m 5.43kb
ex7.pdf 742.07kb
ex7data1.mat 0.97kb
ex7data2.mat 4.67kb
ex7faces.mat 10.52M
ex7_pca.m 7.06kb
featureNormalize.m 0.50kb
findClosestCentroids.m 1.15kb
kMeansInitCentroids.m 0.78kb
pca.m 0.83kb
plotDataPoints.m 0.42kb
plotProgresskMeans.m 0.82kb
projectData.m 0.93kb
recoverData.m 1.00kb
runkMeans.m 1.93kb
submit.m 16.56kb
submitWeb.m 0.79kb
mlclass-ex8-jin
checkCostFunction.m 1.58kb
cofiCostFunc.m 2.19kb
computeNumericalGradient.m 1.07kb
estimateGaussian.m 0.95kb
ex8.m 3.72kb
ex8.pdf 264.67kb
ex8data1.mat 9.28kb
ex8data2.mat 91.29kb
ex8_cofi.m 6.93kb
ex8_movieParams.mat 196.48kb
ex8_movies.mat 218.16kb
fmincg.m 8.54kb
loadMovieList.m 0.64kb
movie_ids.txt 47.31kb
multivariateGaussian.m 0.79kb
normalizeRatings.m 0.47kb
selectThreshold.m 1.45kb
submit.m 17.10kb
submitWeb.m 0.79kb
visualizeFit.m 0.57kb
整合pdf
ex1.pdf 509.33kb
ex2.pdf 188.26kb
ex3.pdf 327.73kb
ex4.pdf 406.78kb
ex5.pdf 181.55kb
ex6.pdf 355.43kb
ex7.pdf 742.07kb
ex8.pdf 264.67kb
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 23.49kb
.gitattributes 0.47kb
.gitignore 2.58kb
coursera作业答案 仅供参考.zip 28.90M
README.md 1.43kb
教程和笔记
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
PotPlayer_1.6.51270.zip 19.22M
网易视频教程
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 0.54kb
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)
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)
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)
01_Linear_Support_Vector_Machine
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
01_Motivation_of_Dual_SVM_15-54.mp4 20.31M
01_Motivation_of_Dual_SVM_15-54.pdf 578.55kb
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
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
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
01_Soft-Margin_SVM_as_Regularized_Model_13-40.mp4 17.01M
01_Soft-Margin_SVM_as_Regularized_Model_13-40.pdf 560.71kb
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
01_Kernel_Ridge_Regression_17-17.mp4 21.43M
01_Kernel_Ridge_Regression_17-17.pdf 752.20kb
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
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
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
01_Decision_Tree_Hypothesis_17-28.mp4 21.83M
01_Decision_Tree_Hypothesis_17-28.pdf 740.49kb
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
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
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
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
01_Deep_Neural_Network_21-30.mp4 27.29M
01_Deep_Neural_Network_21-30.pdf 582.82kb
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
01_RBF_Network_Hypothesis_12-55.mp4 148.35M
01_RBF_Network_Hypothesis_12-55.pdf 949.72kb
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
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 573.01kb
16_Finale
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 458.52kb
炼数成金-机器学习
第1课 机器学习概论
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
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 59.84kb
假设检验讲解.rar 21.25M
薛毅书源程序.rar 68.84kb
第3课 岭回归，Lasso，变量选择技术。案例：凯撒密码破译
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
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 0.05kb
龙星计划_机器学
下载之前必看！更多视频资料下载目录.docx 479.29kb
模式识别_35_国防科学技术大学(蔡宣平)
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)
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 939.50kb
ML_机器学习应用班
第八课
8.mp4 248.47M
第二课
应用班2_1_1h44min.mp4 1.10G
应用班第二课第二部分 .mp4 64.77M
第九课
9-1.mp4 83.05M
9-2.mp4 71.64M
第六课
6-1.mp4 91.84M
6-2.mp4 109.76M
第七课
7-1.flv 144.38M
7-2.mp4 160.88M
第三课
应用班第三节课.mp4 396.21M
第十课
10.mp4 159.58M
第四课
第二部分.mp4 132.05M
应用班第四节课1_1h44_33.mp4 1.09G
第五课
5-1.mp4 1.47G
5-2.mp4 691.44M
第一课
第一课.mp4 178.36M
机器学习应用班第1课数学基础 (1).pdf 415.70kb
机器学习应用班资料.zip 497.25M
算法_10月机器学习算法班
ppt
Thumbs.db 20.00kb
十月算法班第10讲：推荐系统.pdf 11.13M
十月算法班第11讲：CTR预估.pdf 2.20M
十月算法班第12讲：聚类和社交网络算法-10月机器学习算法班.pdf 13.88M
十月算法班第13讲：机器学习算法之图模型初步.pdf 1.74M
十月算法班第15讲：主体模型.pdf 1.46M
十月算法班第16讲：人工神经网络.pdf 23.25M
十月算法班第17讲：计算机视觉与卷积神经网络.pdf 3.62M
十月算法班第18讲：循环神经网络与自然语言处理.pdf 2.14M
十月算法班第19讲：深度学习框架与应用.pdf 6.76M
十月算法班第1讲.pdf 261.58kb
十月算法班第20讲：采样与变分.pdf 2.41M
十月算法班第2讲.pdf 344.85kb
十月算法班第3讲：凸优化初步.pdf 210.55kb
十月算法班第4节：最大熵模型与EM.pdf 4.11M
十月算法班第5讲：决策树随机森林.pdf 2.60M
十月算法班第8讲：机器学习中的特征工程—笔记版.pdf 7.89M
十月算法班第9讲：机器学习调优与融合.pdf 7.18M
源码
Image_seg.zip 1.88M
课程PPT与代码.zip 173.58M
01.第1课 概率论与数理统计.mkv 1.09G
02.第2课 矩阵和线性代数.mkv 925.39M
03.第3课 凸优化.mkv 956.72M
04.第4课 回归.mkv 903.64M
05.第5课 决策树、随机森林.mkv 815.52M
06.第6课 SVM.mkv 826.68M
07.第7课 最大熵与EM算法.mkv 445.11M
08.第8课 特征工程.mkv 1010.87M
09.第9课 模型调优.mkv 927.87M
10.第10课 推荐系统.mkv 782.52M
11.第11课 从分类到CTR预估.mkv 982.44M
12.第12课 聚类.mkv 831.81M
13.第13课 贝叶斯网络.mkv 659.03M
14.第14课 隐马尔科夫模型HMM.mkv 650.75M
15.第15课 主题模型.mkv 1.23G
16.第16课 采样与变分.mkv 775.63M
17.第17课 人工神经网络.mkv 826.79M
18.第18课 深度学习之CNN.mkv 337.87M
19.第19课 深度学习之RNN.mkv 955.13M
20.第20课 深度学习实践.mkv 519.98M
算法_4月机器学习算法班
(01)机器学习与相关数学初步
(1)机器学习初步与微积分概率论.pdf 25.49M
(1)机器学习与相关数学初步.avi 427.96M
(02)数理统计与参数估计
(2)数理统计与参数估计.avi 470.89M
(2)数理统计与参数估计.pdf 6.23M
(03)矩阵分析与应用
(3)矩阵分析与应用.avi 450.44M
(3)矩阵分析与应用.pdf 1.21M
(04)凸优化初步
(4)凸优化初步.avi 453.10M
(4)凸优化初步.pdf 2.90M
(05)回归分析与工程应用
课件和数据及代码
.ipynb_checkpoints
logistic_regression_example-checkpoint.ipynb 146.33kb
Untitled-checkpoint.ipynb 0.07kb
4月班第5课课件：回归及工程应用经验.pdf 10.13M
data1.txt 3.69kb
data2.txt 2.18kb
logistic_regression_example.ipynb 146.33kb
Untitled.ipynb 6.84kb
(5)回归分析与工程应用.avi 500.18M
(06)特征工程
课件与数据及代码
4月班第6课课件：特征工程.pdf 6.84M
feature_engineering_example.ipynb 192.20kb
kaggle_bike_competition_train.csv 594.49kb
(6)特征工程.avi 731.61M
(07)工作流程与模型调优
(7)工作流程与模型调优.avi 763.80M
(7)工作流程与模型调优.zip 6.15M
(08)最大熵模型与EM算法
(8)最大熵模型与EM算法.avi 445.37M
(8)最大熵模型与EM算法.pdf 2.78M
(09)推荐系统与应用
(9)推荐系统与应用
4月机器学习班第9课–推荐系统.pdf 9.57M
CF&&MF recommendation system.zip 11.06M
Reccomendation System Examples.ipynb 11.93kb
(9)推荐系统与应用.avi 702.91M
(10)聚类算法与应用
(10)聚类算法与应用.avi 727.71M
(10)聚类算法与应用.pdf 13.98M
(11)决策树随机森林和adaboost
代码
.ipynb_checkpoints
随机森林-checkpoint.ipynb 20.34kb
randomforests.py 1.68kb
randomforests.pyc 1.69kb
samtrain.csv 553.67kb
samval.csv 802.63kb
随机森林.ipynb 20.34kb
(11)决策树随机森林adaboost.avi 408.17M
(11)决策树随机森林adaboost.pdf 1.10M
(12)SVM
(补充材料1)SVM补充视频
补充SVM视频下载地址.txt 0.05kb
(补充材料2)SVM的Python程序代码
sklearnExample.py 1.24kb
(12)SVM.avi 487.86M
(12)SVM.pdf 5.42M
(12)支持向量机.ipynb 325.99kb
(13)贝叶斯方法
(13)贝叶斯方法.avi 435.48M
(13)贝叶斯方法.pdf 6.21M
naive_bayes-master.zip 481.03kb
(14)主题模型
(14)主题模型.avi 437.07M
(14)主题模型.pdf 896.27kb
(补充阅读材料1)Comparing LDA with pLSI as a Dimensionality Reduction Method in Document Clustering.pdf 464.39kb
(补充阅读材料2)Investigating task performance of probabilistic topic models – an empirical study of PLSA and LDA.pdf 770.75kb
LDAClassify.zip 6.17kb
(15)贝叶斯推理采样与变分
(15)贝叶斯推理-采样与变分简介.pdf 1.02M
(15)贝叶斯推理采样变分方法.avi 323.43M
gibbsGauss.py 0.46kb
(16)人工神经网络
(16)人工神经网络.avi 391.51M
(16)人工神经网络.pdf 6.68M
Lesson_16_Neural_network_example.ipynb 13.76kb
(17)卷积神经网络
(17)卷积神经网络.avi 476.19M
(17)卷积神经网络.pdf 23.65M
(18)循环神经网络与LSTM
(18)循环神经网络和LSTM.avi 419.65M
(18)循环神经网络与LSTM.pdf 3.32M
(19)Caffe&Tensor Flow&MxNet 简介
(19)Caffe&Tensor Flow&MxNet 简介.avi 401.93M
(19)Caffe&Tensor Flow&MxNet 简介.pdf 22.02M
(20)贝叶斯网络和HMM
(20)贝叶斯网络和HMM.avi 405.42M
(20)贝叶斯网络和HMM.pdf 1.86M
(额外补充)词嵌入word embedding
(额外补充)词嵌入word embedding.avi 471.74M
(额外补充)词嵌入原理及应用简介.pdf 816.13kb