File List
-
homeworks/discusstion/MLF_hw1.zip 23.47 KB
homeworks/discusstion/机器学习基石心得1 By Loveisp.docx 940.23 KB
homeworks/discusstion/机器学习基石心得3-第一次作业下.docx 117.21 KB
homeworks/Homework #0.pdf 118.96 KB
homeworks/Homework #1/data.xls 137 KB
homeworks/Homework #1/error_date.m 249 B
homeworks/Homework #1/hw1.dat 13.79 KB
homeworks/Homework #1/hw2.dat 17.37 KB
homeworks/Homework #1/hw3.dat 17.46 KB
homeworks/Homework #1/panbieshi.m 114 B
homeworks/Homework #1/PLA_v1.m 404 B
homeworks/Homework #1/PLA_v2.m 1004 B
homeworks/Homework #1/PLA_v3.m 445 B
homeworks/Homework #1/Pocket.m 1.47 KB
homeworks/Homework #1/一些指令.txt 219 B
homeworks/Homework #2/data.xlsx 105.29 KB
homeworks/Homework #2/data_test.dat 59.23 KB
homeworks/Homework #2/data_train.dat 5.98 KB
homeworks/Homework #2/draft1.m 2.1 KB
homeworks/Homework #2/draft2.m 1.98 KB
homeworks/Homework #2/test.m 2.41 KB
homeworks/Homework #2/test20.m 633 B
homeworks/Homework #2/计算过程.xlsx 14.06 KB
homeworks/Homework #3/ML/data.xlsx 105.29 KB
homeworks/Homework #3/ML/data_test.dat 59.23 KB
homeworks/Homework #3/ML/data_train.dat 5.98 KB
homeworks/Homework #3/ML/draft1.m 2.1 KB
homeworks/Homework #3/ML/draft2.m 1.98 KB
homeworks/Homework #3/ML/hw13.m 1 KB
homeworks/Homework #3/ML/hw14.m 1.55 KB
homeworks/Homework #3/ML/hw18.m 1.04 KB
homeworks/Homework #3/ML/hw3_Ein_d.m 784 B
homeworks/Homework #3/ML/hw3_test.dat 478.63 KB
homeworks/Homework #3/ML/hw3_train.dat 159.54 KB
homeworks/Homework #3/ML/hw7.m 417 B
homeworks/Homework #3/ML/hw9.m 612 B
homeworks/Homework #3/ML/test.m 2.41 KB
homeworks/Homework #3/ML/test20.m 633 B
homeworks/Homework #3/ntumlone-hw3-hw3_test.dat 478.63 KB
homeworks/Homework #3/ntumlone-hw3-hw3_train.dat 159.54 KB
homeworks/Homework #3/QQ图片20140113175847.jpg 159.38 KB
homeworks/Homework #3/作业中较难的那几题的参考材料.zip 406.99 KB
homeworks/Homework #3/理解newton direction.pdf 270.63 KB
homeworks/Homework #4/HW4_13.m 1.59 KB
homeworks/Homework #4/HW4_15.m 1.49 KB
homeworks/Homework #4/HW4_19.m 4.69 KB
homeworks/Homework #4/hw4_test.dat 20.03 KB
homeworks/Homework #4/hw4_train.dat 4 KB
homeworks/Homework #4/w_reg.m 180 B
Learning From Data 2nd Ed (Wiley,2007).pdf 4.03 MB
ppt/lecture_slides-01_handout.pdf 6.45 MB
ppt/lecture_slides-02_handout.pdf 800.28 KB
ppt/lecture_slides-03_handout.pdf 816.43 KB
ppt/lecture_slides-04_handout.pdf 1.8 MB
ppt/lecture_slides-05_handout .pdf 446.18 KB
ppt/lecture_slides-06_handout.pdf 557.49 KB
ppt/lecture_slides-07_handout.pdf 487.03 KB
ppt/lecture_slides-08_handout.pdf 937.54 KB
ppt/lecture_slides-09_handout.pdf 799.53 KB
ppt/lecture_slides-10_handout.pdf 526.1 KB
ppt/lecture_slides-11_handout.pdf 604.81 KB
ppt/lecture_slides-12_handout.pdf 1.19 MB
ppt/lecture_slides-13_handout.pdf 818.36 KB
ppt/lecture_slides-14_handout.pdf 878.39 KB
ppt/lecture_slides-15_handout.pdf 1.09 MB
ppt/lecture_slides-16_handout.pdf 4.88 MB
video/1 - 1 - Course Introduction (10-58).mp4 13.79 MB
video/1 - 2 - What is Machine Learning (18-28).mp4 15.94 MB
video/1 - 3 - Applications of Machine Learning (18-56).mp4 22.31 MB
video/1 - 4 - Components of Machine Learning (11-45).mp4 10.66 MB
video/1 - 5 - Machine Learning and Other Fields (10-21).mp4 11.97 MB
video/10 - 1 - Logistic Regression Problem (14-33).mp4 11.94 MB
video/10 - 2 - Logistic Regression Error (15-58).mp4 11.96 MB
video/10 - 3 - Gradient of Logistic Regression Error (15-38).mp4 12.37 MB
video/10 - 4 - Gradient Descent (19-18).mp4 14.91 MB
video/11 - 1 - Linear Models for Binary Classification (21-35).mp4 16.91 MB
video/11 - 2 - Stochastic Gradient Descent (11-39).mp4 9.96 MB
video/11 - 3 - Multiclass via Logistic Regression (14-18).mp4 11.28 MB
video/11 - 4 - Multiclass via Binary Classification (11-35).mp4 9.36 MB
video/12 - 1 - Quadratic Hypothesis (23-47).mp4 17.92 MB
video/12 - 2 - Nonlinear Transform (09-52).mp4 8.03 MB
video/12 - 3 - Price of Nonlinear Transform (15-37).mp4 12.55 MB
video/12 - 4 - Structured Hypothesis Sets (09-36).mp4 7.31 MB
video/13 - 1 - What is Overfitting- (10-45).mp4 9.01 MB
video/13 - 2 - The Role of Noise and Data Size (13-36).mp4 11.4 MB
video/13 - 3 - Deterministic Noise (14-07).mp4 11.92 MB
video/13 - 4 - Dealing with Overfitting (10-49).mp4 8.81 MB
video/14 - 1 - Regularized Hypothesis Set (19-16).mp4 15.18 MB
video/14 - 2 - Weight Decay Regularization (24-08).mp4 18.54 MB
video/14 - 3 - Regularization and VC Theory (08-15).mp4 7.14 MB
video/14 - 4 - General Regularizers (13-28).mp4 11.24 MB
video/15 - 1 - Model Selection Problem (16-00).mp4 13.26 MB
video/15 - 2 - Validation (13-24).mp4 10.47 MB
video/15 - 3 - Leave-One-Out Cross Validation (16-06).mp4 12.27 MB
video/15 - 4 - V-Fold Cross Validation (10-41).mp4 9.17 MB
video/16 - 1 - Occam-'s Razor (10-08).mp4 8.21 MB
video/16 - 2 - Sampling Bias (11-50).mp4 10.26 MB
video/16 - 3 - Data Snooping (12-28).mp4 10.8 MB
video/16 - 4 - Power of Three (08-49).mp4 7.55 MB
video/2 - 1 - Perceptron Hypothesis Set (15-42).mp4 18.55 MB
video/2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp4 16.61 MB
video/2 - 3 - Guarantee of PLA (12-37).mp4 14.45 MB
video/2 - 4 - Non-Separable Data (12-55).mp4 33.75 MB
video/3 - 1 - Learning is Impossible- (13-32).mp4 52.41 MB
video/3 - 2 - Probability to the Rescue (11-33).mp4 46.3 MB
video/3 - 3 - Connection to Learning (16-46).mp4 72.64 MB
video/3 - 4 - Connection to Real Learning (18-06).mp4 78.91 MB
video/4 - 1 - Learning with Different Output Space (17-26).mp4 76.25 MB
video/4 - 2 - Learning with Different Data Label (18-12).mp4 50.14 MB
video/4 - 3 - Learning with Different Protocol (11-09).mp4 31.41 MB
video/4 - 4 - Learning with Different Input Space (14-13).mp4 40.89 MB
video/5 - 1 - Recap and Preview (13-44).mp4 11.35 MB
video/5 - 2 - Effective Number of Lines (15-26).mp4 12.57 MB
video/5 - 3 - Effective Number of Hypotheses (16-17).mp4 13.12 MB
video/5 - 4 - Break Point (07-44).mp4 6.6 MB
video/6 - 1 - Restriction of Break Point (14-18).mp4 11.52 MB
video/6 - 2 - Bounding Function- Basic Cases (06-56).mp4 5.5 MB
video/6 - 3 - Bounding Function- Inductive Cases (14-47).mp4 11.64 MB
video/6 - 4 - A Pictorial Proof (16-01).mp4 12.85 MB
video/7 - 1 - Definition of VC Dimension (13-10).mp4 10.67 MB
video/7 - 2 - VC Dimension of Perceptrons (13-27).mp4 9.97 MB
video/7 - 3 - Physical Intuition of VC Dimension (6-11).mp4 5.16 MB
video/7 - 4 - Interpreting VC Dimension (17-13).mp4 13.55 MB
video/8 - 1 - Noise and Probabilistic Target (17-01).mp4 13.93 MB
video/8 - 2 - Error Measure (15-10).mp4 11.4 MB
video/8 - 3 - Algorithmic Error Measure (13-46).mp4 10.98 MB
video/8 - 4 - Weighted Classification (16-54).mp4 13.11 MB
video/9 - 1 - Linear Regression Problem (10-08).mp4 8.04 MB
video/9 - 2 - Linear Regression Algorithm (20-03).mp4 14.51 MB
video/9 - 3 - Generalization Issue (20-34).mp4 15.28 MB
video/9 - 4 - Linear Regression for Binary Classification (11-23).mp4 9.05 MB
延伸阅读.doc 34 KB
Download Info
-
Tips
“机器学习基石” Its related downloads are collected from the DHT sharing network, the site will be 24 hours of real-time updates, to ensure that you get the latest resources.This site is not responsible for the authenticity of the resources, please pay attention to screening.If found bad resources, please send a report below the right, we will be the first time shielding.
-
DMCA Notice and Takedown Procedure
If this resource infringes your copyright, please email([email protected]) us or leave your message here ! we will block the download link as soon as possiable.