transformer by hand
https://aibyhand.substack.com/p/8-can-you-calculate-a-transformer
https://www.youtube.com/watch?v=v14_yAqY0X0
Showing posts with label star. Show all posts
Showing posts with label star. Show all posts
Sunday, July 7, 2024
Monday, December 25, 2023
*** Journals related to quantitative biology, mathematic biology, computational biology, aging
Journals related to quantitative biology, mathematic biology, computational biology, aging
-> scientific reports
-> scientific reports
-> Patterns
-> ACM/IEEE bioinformatics
-> Springer, Bulletin of Mathematical Biology impact factor 2.0
-> Mathematical Biosciences
E Voit (editor)
-> Elsiver Jounrnal of computational and applied mathematics, impact factor 1.1
http://www.journals.elsevier.com/journal-of-computational-and-applied-mathematics/
-> Springer, Journal of Mathematical biology, impact factor 2-3.
http://link.springer.com/journal/40484/2/1/page/1
>
http://en.wikipedia.org/wiki/List_of_mathematics_journals
>BMC systems biology, impact factor 3.1
>BMC Biology
http://www.biomedcentral.com/bmcbiol/about#publication
- The IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB).
- The Journal of Computational Biology.
- The Bulletin of Mathematical Biology
http://www.bio.vu.nl/nvtb/JournalsTB.html
->Aging cell, Impact factor 6 http://www.bioxbio.com/if/html/AGING-CELL.html
Open access, publication charge
http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291474-9726/homepage/ForAuthors.html
-> Peer J
-> Journal of Aging Research. Gavirolv and Gavrilova's journal.
-> Journal of theoretic biology, impact factor 2.5
Biology Open, BMC Biology, the four EMBO scientific publications, and the PLOS journals.
Genetics
Royal society transactions
Aging, New York
https://www.aging-us.com/editorial-board
FEMS Yeast
impact factor 2.4
Mechanism of aging and development, IF 3.2, http://www.medsciediting.com/sci/?fullname=mechanisms%20of%20aging%20%20development&action=search
Open access is a choice
Experimental gerontology, IF 3.9
http://www.bioxbio.com/if/html/EXP-GERONTOL.html
Eukaryotic cell, IF 3.6
HHMI journal
http://www.medsciediting.com/sci/?fullname=mechanisms%20of%20aging%20%20development&action=search
computational biology and bioinformatics research
https://academicjournals.org/journal/JCBBR
IF 7.2
https://www.sciencedirect.com/journal/computational-and-structural-biotechnology-journal/about/aims-and-scope
IEEE BIBM
https://web.cvent.com/event/cc0e2184-2d99-455d-80bb-41c43208970e/summary
Sunday, November 8, 2020
talk: make coding fun and relevant for education with real world data analysis
make coding fun and relevant for education with real world data analysis
small college
cloud computing, big tech should provide education platform for free to K12 and small colleges
start with the kids
Thursday, January 29, 2015
Data driven investigator, Moore
http://ged.msu.edu/downloads/2014-moore-ddd-preapp.pdf
http://www.moore.org/programs/science/data-driven-discovery/ddd-investigators
Sunday, January 11, 2015
XSEDE trial, 20150111Sun, blacklight, OSG
Byte:xsede hqin$ ssh hongqin@login.xsede.org
hongqin@login.xsede.org's password:
Last login: Wed Aug 13 17:38:55 2014 from spelman-fw.spelman.edu
Welcome to the XSEDE Single Sign-On (SSO) Hub!
Your storage on this machine is limited to 100MB.
You may connect from here to any XSEDE resource on which you have an account.
To view a list of sites where you actually have an account, visit:
https://portal.xsede.org/group/xup/accounts
Here are the login commands for common XSEDE resources:
Blacklight: gsissh blacklight.psc.xsede.org
Darter: gsissh gsissh.darter.nics.xsede.org
Gordon Compute Cluster: gsissh gordon.sdsc.xsede.org
Gordon ION: gsissh gordon.sdsc.xsede.org
Keeneland: gsissh gsissh.keeneland.gatech.xsede.org
Mason: gsissh mason.iu.xsede.org
Maverick: gsissh -p 2222 maverick.tacc.xsede.org
Nautilus: gsissh gsissh.nautilus.nics.xsede.org
Open Science Grid: gsissh submit-1.osg.xsede.org
Stampede: gsissh -p 2222 stampede.tacc.xsede.org
SuperMIC: gsissh -p 2222 supermic.cct-lsu.xsede.org
Trestles: gsissh trestles.sdsc.xsede.org
Contact help@xsede.org for any assistance that may be needed.
[hongqin@gw69 ~]$ gsissh blacklight.psc.xsede.org
Last login: Thu Aug 14 21:14:38 2014 from 184.77.11.206
Pittsburgh Supercomputing Center
This system is for the use of authorized users only. Unauthorized use may
be monitored and recorded. In the course of such monitoring or through
system maintenance, the activities of authorized users may be monitored.
By using this system you expressly consent to such monitoring.
Blacklight is unique, for optimal performance see http://www.psc.edu/index.php/computing-resources/blackligh
hqin2@tg-login1:~> module load R
hqin2@tg-login1:~> R -f test.R
#nano -w job1.sh
hqin2@tg-login1:~> module load R
hqin2@tg-login1:~> R -f test.R
hqin2@tg-login1:~> sh job1.sh
job1.sh: line 1: module: command not found
R version 2.15.3 (2013-03-01) -- "Security Blanket"
Copyright (C) 2013 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-unknown-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x = 4*5
> write(x, "x.txt")
>
hqin2@tg-login1:~> qsub job1.sh
418479.tg-login1.blacklight.psc.teragrid.org
#in the queue as show by qstat
___________________
Note On 2015Jan13Tue,
I logged to blacklight and found the job was executed correctly
hqin2@tg-login1:~> cat job1.sh.e418479
/var/spool/torque/mom_priv/jobs/418479.tg-login1.blacklight.psc.teragrid.org.SC: line 1: module: command not found
/var/spool/torque/mom_priv/jobs/418479.tg-login1.blacklight.psc.teragrid.org.SC: line 2: R: command not found
__________________
hqin2@tg-login1:~> exit
logout
Connection to blacklight.psc.xsede.org closed.
[hongqin@gw69 ~]$ gsissh submit-1.osg.xsede.org
You have 1 active projects you can charge jobs to.
Project Name Balance (CPU Hours) End Date
-------------------------------- ------------------- ------------
TG-MCB140211 100000 08/08/2015
Note that you can still charge to a project with a negative balance, as
long as the project has not reached its end date. A project with a
negative balance may result in jobs being given lower priority.
When submitting jobs, please specify what project to charge to with a
+ProjectName line in your HTCondor submit file. For example:
+ProjectName = "TG-MCB140211"
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
Your current filesystem usages and quotas are:
/home 0% used (28.0 KB of 20.0 GB)
/local-scratch 0% used (0.0 B of 2.0 TB)
NOTE: The /local-scratch filesystem automatically deletes files older than
90 days.
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
2015-01-12 03:27:40 UTC [hongqin@xd-login:~]$
2015-01-12 03:43:03 UTC [hongqin@xd-login:~]$ cat testOSH.sub
universe = vanilla
+ProjectName = "TG-MCB140211"
executable = testOSH.sh
output = job.out
error = job.err
log = job.log
notification = NEVER
queue
[hongqin@gw69 ~]$ pwd
/home/hongqin
[hongqin@gw69 ~]$ gsissh blacklight.psc.xsede.org
Last login: Sun Jan 11 22:04:05 2015 from gw69.iu.xsede.org
Pittsburgh Supercomputing Center
This system is for the use of authorized users only. Unauthorized use may
be monitored and recorded. In the course of such monitoring or through
system maintenance, the activities of authorized users may be monitored.
By using this system you expressly consent to such monitoring.
Blacklight is unique, for optimal performance see http://www.psc.edu/index.php/computing-resources/blacklight
hqin2@tg-login1:~> ls
job1.sh job2.pbs test.R test.txt x.txt
hqin2@tg-login1:~> ll
total 20
-rw-r--r-- 1 hqin2 mc48o9p 26 2015-01-11 22:13 job1.sh
-rw-r--r-- 1 hqin2 mc48o9p 38 2015-01-11 22:23 job2.pbs
-rw-r--r-- 1 hqin2 mc48o9p 26 2014-08-13 17:42 test.R
-rw-r--r-- 1 hqin2 mc48o9p 12 2014-08-13 17:41 test.txt
-rw-r--r-- 1 hqin2 mc48o9p 3 2015-01-11 22:13 x.txt
hqin2@tg-login1:~> qstat | grep qin
418479.tg-login1 job1.sh hqin2 0 Q batch_r
done, pool single gene mutant data from rls.csv
I studied ken's old codes, my old codes, and wrote "_export_rls20150111.R".
On 20150111, I found out that I did the work in "_explore.20140705.R".
On 20150111, I found out that I did the work in "_explore.20140705.R".
Thursday, January 8, 2015
Cell cycle, MSH2, mutagen treatment
A Gammie:
"We have never observed a growth defect with the null or any of the mutants, but we think there might be some differences in the presence of DNA damaging agents (HU or cisplatin) – you could try that for your flow experiments. I wonder if your strains have picked up secondary mutations. We never propagate the strains for very long because they are mutators and pick up deleterious mutations during propagation."
Gammie used HU and MMS in her 2013 DNA repair paper
http://www.sciencedirect.com/science/article/pii/S1568786412002819
HU was 0.1M = 100mM and MMS was 0.04% in this paper. Treatments were 1.5 hours (about one generation time for the BY lab strain).
Hydroxyurea (HU) (no respiratory risk label on Sigma)
AKA, hydroxycarbamidehttp://en.wikipedia.org/wiki/Hydroxycarbamide
http://www.sigmaaldrich.com/content/dam/sigma-aldrich/docs/Sigma/Product_Information_Sheet/2/h8627pis.pdf
FW: 76.06,
So, 1 gram can make 2M solution of 1000x2 /76.06 mL = 13.1/2 = 6.5 mL (This is 1000/6.5 mg/mL = 154 mg/mL)
"Hydroxyurea is freely soluble in water, to at least 50 mg/ml (5%). However, because hydroxyurea decomposes in the presence of moisture, aqueous solutions are probably not stable. It is recommended to store hydroxy-urea at 2-8 °C. Hydroxyurea should be stored in a dry atmosphere in airtight containers." (Sigma)
Hydroxyurea (Hydroxycarbamide) ab142613 - Abcam
www.abcam.com/Hydroxyurea-Hydroxycarbamide-ab142613.pdf
Claim 100mM stock solution in water or DMSO?
http://www.genetics.org/content/189/2/533.full.pdf+html
Tripathi2011Genetics: HU cause G2/M arrest in yeast cells.
====================
Methyl methanesulfonate (MMS) (respiratory risk, require facial mask)
| Molar mass | 110.13 g/mol |
http://www.sigmaaldrich.com/catalog/product/aldrich/129925?lang=en®ion=US
http://en.wikipedia.org/wiki/Methyl_methanesulfonate
Cisplatin (very expensive at Sigma)
Need to know to their dosage to conclude which one is actually more expensive to do experiments.
References:
http://hongqinlab.blogspot.com/2013/12/sce-cell-cycle-and-morphology.htmlSaturday, January 3, 2015
compare result table and rls.csv
rls.csv contains 50681 observations of 32 variables
in sqlite3 on rls.db
select max(id) from result
/* this is 50689 */
So, result table in rls.db contains similar entries in rls.csv.
in sqlite3 on rls.db
select max(id) from result
/* this is 50689 */
So, result table in rls.db contains similar entries in rls.csv.
Monday, December 29, 2014
diagonal matrix of essential genes in network aging model
diagonal matrix of essential genes in network aging model
A^k = P D P^-1
If the diagonal matrix contains only the number of links of essential genes, its decaying might be easily computed numerically.
Diagonalization of A can be found through eigen values and eigen vectors.
A^k = P D P^-1
If the diagonal matrix contains only the number of links of essential genes, its decaying might be easily computed numerically.
Diagonalization of A can be found through eigen values and eigen vectors.
Saturday, December 27, 2014
SQLite3 code on rls.db
file 'test_rls.sql'
.open rls.db
.databases
.tables
.separator ::
.headers on
.mode column
select distinct experiment from result_experiment limit 20;
.indices
.indices set
.width 5
select * from result limit 1;
/* The following select can take rls and its reference rls */
select experiments,set_name,set_strain,set_background,set_genotype,
set_lifespan_mean,ref_genotype,ref_lifespan_mean
from result limit 2;
/* The fields of set_name and set_genotype sometimes provide the ORF-name pair, but there are many exceptions. */
.open rls.db
.databases
.tables
.separator ::
.headers on
.mode column
select distinct experiment from result_experiment limit 20;
.indices
.indices set
.width 5
select * from result limit 1;
/* The following select can take rls and its reference rls */
select experiments,set_name,set_strain,set_background,set_genotype,
set_lifespan_mean,ref_genotype,ref_lifespan_mean
from result limit 2;
/* The fields of set_name and set_genotype sometimes provide the ORF-name pair, but there are many exceptions. */
SQLite 3, osX, byte, rls.db
Reference: http://www.sqlite.org/cli.html
#I want to install SQLite to load 'rls.db'.
$ sudo port install sqlite3
#OK
#how to load 'rls.db' ?
#Notes, field 'experiments' in 'result' maybe used to find the in-experiment wildtype controls.
# Ken once suggested that "pooled by" column?? file, genotype, mixed
# set lifespan
# ref lifespan
select experiments,set_name,set_strain,set_background,set_genotype,
set_lifespan_mean,ref_genotype,ref_lifespan_mean
from result limit 2;
#how to load 'rls.db' ?
$ sqlite3
SQLite version 3.8.7.4 2014-12-09 01:34:36
Enter ".help" for usage hints.
Connected to a transient in-memory database.
Use ".open FILENAME" to reopen on a persistent database.
sqlite> .open rls.db
sqlite> .databases
seq name file
--- --------------- ----------------------------------------------------------
0 main /Users/hqin/projects/0.network.aging.prj/4.svm/rls.db
sqlite> .tables
build_log genotype_pubmed_id result_experiment set
cross_mating_type meta result_ref yeast_strain
cross_media result result_set
sqlite> .indices
build_log_filename
cross_mating_type_background
cross_mating_type_genotype
cross_mating_type_locus_tag
cross_mating_type_media
cross_mating_type_temperature
cross_media_background
cross_media_genotype
cross_media_locus_tag
cross_media_mating_type
cross_media_temperature
genotype_pubmed_id_genotype
genotype_pubmed_id_pubmed_id
meta_name
result_experiment_experiment
result_experiment_result_id
result_percent_change
result_pooled_by
result_ranksum_p
result_ref_background
result_ref_genotype
result_ref_locus_tag
result_ref_mating_type
result_ref_media
result_ref_name
result_ref_result_id
result_ref_set_id
result_ref_strain
result_ref_temperature
result_set_background
result_set_genotype
result_set_lifespan_mean
result_set_locus_tag
result_set_mating_type
result_set_media
result_set_name
result_set_result_id
result_set_set_id
result_set_strain
result_set_temperature
set_experiment
set_media
set_name
set_strain
set_temperature
yeast_strain_background
yeast_strain_genotype_short
yeast_strain_genotype_unique
yeast_strain_mating_type
yeast_strain_name
yeast_strain_owner
sqlite> select distinct experiment from result_experiment limit 20;
experiment
1
100
101
102_plate115
103
104
105
106_plate116
107
108_plate117
...
sqlite> .separator :::
sqlite> select * from result limit 2;
id:::experiments:::set_name:::set_strain:::set_background:::set_mating_type:::set_locus_tag:::set_genotype:::set_media:::set_temperature:::set_lifespan_start_count:::set_lifespan_count:::set_lifespan_mean:::set_lifespan_stdev:::set_lifespans:::ref_name:::ref_strain:::ref_background:::ref_mating_type:::ref_locus_tag:::ref_genotype:::ref_media:::ref_temperature:::ref_lifespan_start_count:::ref_lifespan_count:::ref_lifespan_mean:::ref_lifespan_stdev:::ref_lifespans:::percent_change:::ranksum_u:::ranksum_p:::pooled_by
1:::127:::BY4741:::KK19:::BY4741:::MATa::::::BY4741:::YPD:::30.0:::20:::20:::30.3:::7.526095:::23,26,34,31,22,37,26,39,22,36,38,24,36,40,26,38,38,17,34,19:::BY4742:::DH502:::BY4742:::MATalpha::::::BY4742:::YPD:::30.0:::40:::40:::29.625:::8.279377:::36,26,15,28,16,44,40,28,25,32,24,29,39,37,30,31,14,17,29,28,44,27,38,29,26,39,38,32,34,33,32,38,16,28,31,11,20,39,30,32:::2.278481:::409.0:::0.8916505557143:::file
2:::127:::ymr226c:::DC:4G4:::BY4741:::MATa::::::tma29:::YPD:::30.0:::20:::20:::27.1:::11.702:::24,11,37,32,41,38,12,11,31,23,39,36,22,19,28,36,24,49,24,5:::BY4741:::KK19:::BY4741:::MATa::::::BY4741:::YPD:::30.0:::20:::20:::30.3:::7.526095:::23,26,34,31,22,37,26,39,22,36,38,24,36,40,26,38,38,17,34,19:::-10.56106:::169.5:::0.4163969339623:::file#Notes, field 'experiments' in 'result' maybe used to find the in-experiment wildtype controls.
# Ken once suggested that "pooled by" column?? file, genotype, mixed
# set lifespan
# ref lifespan
select experiments,set_name,set_strain,set_background,set_genotype,
set_lifespan_mean,ref_genotype,ref_lifespan_mean
from result limit 2;
Sunday, December 21, 2014
Braunewell and Bornholdt, 2009, reliability of network
PB09JTB
reliability of attractors
boolean network dynamics
See also
investigate the interplay of topological structure and dynamical robustness.
boolean network dynamics
The reliability criteriont was used to show the robustness of the yeast cell-
cycle dynamics against timing perturbations (Braunewell and Bornholdt, 2007)
See also
Wednesday, December 10, 2014
Shedden et al, 2008, Nature medicine, gene expression based survival prediction in lung adenocarcinoma
Shedden et al, 2008, Nature medicine, gene expression based survival prediction in lung adenocarcinoma
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2667337/
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2667337/
health disparity, expression dataset, supposed to have ethnic group information.
several classifier algorithms were used. cross-validation were applied, ROC curves used.
good reference for my lifespan prediction study.
Method A (Gene clusters and ridge regression)
binary tree-structured vecgor quantization -> binary cluster tree using expression profiles
Method B (Stratified Cox model on univariately selected genes)
Method C (clustering of samples combined with minimum gene selection).
Method D. (clustering of samples combined with minimum gene selection).
Wednesday, November 26, 2014
Elements of Statistical Learning, video, pdf, Hastie, Tibshirani, 2014
Cloned from R-Blogger.
In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book.
In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book.
If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors’ website.
If you decide to attempt the exercises at the end of each chapter, there is a GitHub repository of solutions provided by students you can use to check your work.
As a supplement to the textbook, you may also want to watch the excellent course lecture videos (linked below), in which Dr. Hastie and Dr. Tibshirani discuss much of the material. In case you want to browse the lecture content, I’ve also linked to the PDF slides used in the videos.
Chapter 1: Introduction (slides, playlist)
- Opening Remarks and Examples (18:18)
- Supervised and Unsupervised Learning (12:12)
Chapter 2: Statistical Learning (slides, playlist)
- Statistical Learning and Regression (11:41)
- Curse of Dimensionality and Parametric Models (11:40)
- Assessing Model Accuracy and Bias-Variance Trade-off (10:04)
- Classification Problems and K-Nearest Neighbors (15:37)
- Lab: Introduction to R (14:12)
Chapter 3: Linear Regression (slides, playlist)
- Simple Linear Regression and Confidence Intervals (13:01)
- Hypothesis Testing (8:24)
- Multiple Linear Regression and Interpreting Regression Coefficients (15:38)
- Model Selection and Qualitative Predictors (14:51)
- Interactions and Nonlinearity (14:16)
- Lab: Linear Regression (22:10)
Chapter 4: Classification (slides, playlist)
- Introduction to Classification (10:25)
- Logistic Regression and Maximum Likelihood (9:07)
- Multivariate Logistic Regression and Confounding (9:53)
- Case-Control Sampling and Multiclass Logistic Regression (7:28)
- Linear Discriminant Analysis and Bayes Theorem (7:12)
- Univariate Linear Discriminant Analysis (7:37)
- Multivariate Linear Discriminant Analysis and ROC Curves (17:42)
- Quadratic Discriminant Analysis and Naive Bayes (10:07)
- Lab: Logistic Regression (10:14)
- Lab: Linear Discriminant Analysis (8:22)
- Lab: K-Nearest Neighbors (5:01)
Chapter 5: Resampling Methods (slides, playlist)
- Estimating Prediction Error and Validation Set Approach (14:01)
- K-fold Cross-Validation (13:33)
- Cross-Validation: The Right and Wrong Ways (10:07)
- The Bootstrap (11:29)
- More on the Bootstrap (14:35)
- Lab: Cross-Validation (11:21)
- Lab: The Bootstrap (7:40)
Chapter 6: Linear Model Selection and Regularization (slides, playlist)
- Linear Model Selection and Best Subset Selection (13:44)
- Forward Stepwise Selection (12:26)
- Backward Stepwise Selection (5:26)
- Estimating Test Error Using Mallow’s Cp, AIC, BIC, Adjusted R-squared (14:06)
- Estimating Test Error Using Cross-Validation (8:43)
- Shrinkage Methods and Ridge Regression (12:37)
- The Lasso (15:21)
- Tuning Parameter Selection for Ridge Regression and Lasso (5:27)
- Dimension Reduction (4:45)
- Principal Components Regression and Partial Least Squares (15:48)
- Lab: Best Subset Selection (10:36)
- Lab: Forward Stepwise Selection and Model Selection Using Validation Set (10:32)
- Lab: Model Selection Using Cross-Validation (5:32)
- Lab: Ridge Regression and Lasso (16:34)
Chapter 7: Moving Beyond Linearity (slides, playlist)
- Polynomial Regression and Step Functions (14:59)
- Piecewise Polynomials and Splines (13:13)
- Smoothing Splines (10:10)
- Local Regression and Generalized Additive Models (10:45)
- Lab: Polynomials (21:11)
- Lab: Splines and Generalized Additive Models (12:15)
Chapter 8: Tree-Based Methods (slides, playlist)
- Decision Trees (14:37)
- Pruning a Decision Tree (11:45)
- Classification Trees and Comparison with Linear Models (11:00)
- Bootstrap Aggregation (Bagging) and Random Forests (13:45)
- Boosting and Variable Importance (12:03)
- Lab: Decision Trees (10:13)
- Lab: Random Forests and Boosting (15:35)
Chapter 9: Support Vector Machines (slides, playlist)
- Maximal Margin Classifier (11:35)
- Support Vector Classifier (8:04)
- Kernels and Support Vector Machines (15:04)
- Example and Comparison with Logistic Regression (14:47)
- Lab: Support Vector Machine for Classification (10:13)
- Lab: Nonlinear Support Vector Machine (7:54)
Chapter 10: Unsupervised Learning (slides, playlist)
- Unsupervised Learning and Principal Components Analysis (12:37)
- Exploring Principal Components Analysis and Proportion of Variance Explained (17:39)
- K-means Clustering (17:17)
- Hierarchical Clustering (14:45)
- Breast Cancer Example of Hierarchical Clustering (9:24)
- Lab: Principal Components Analysis (6:28)
- Lab: K-means Clustering (6:31)
- Lab: Hierarchical Clustering (6:33)
Interviews (playlist)
- Interview with John Chambers (10:20)
- Interview with Bradley Efron (12:08)
- Interview with Jerome Friedman (10:29)
- Interviews with statistics graduate students (7:44)
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