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ee.pdf

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Useful info:

Presentation on Apr 30 (starting at 8:30)

  • Applying text mining to detect opinions on climate change: Maria and Emily
  • Statistical analysis and some machine learning applications on interpreting Ribisome footprint data : Lu
  • Wrapping back on the World of Code and Developer Profiles: Andrey Karnauch
  • Daniel

Presentation on Apr 23

  • Self-organizing maps and applications in geospatial data: Linsey
  • Image Processing: Ethan

Presentation Apr 16

  • Tanner: D3

Presentation Apr 9

  • Guest presentation by Eduardo: computational aspects of Doc2Vec

Presentations Apr 2

  • Jerry: Chaos Monkey
  • Self Driving Cars: Shang

Presentations March 26

  • Evan: Recommender systems
  • Trish: Known Unknowns - testing recommender systems (tentative)

Presentations March 12

  • Dustin: SVM & SVR
  • Emily: text feature representation (tentative)
  • recording

Presentations March 5

Presentations Feb 26

  • Dakota: Security

Presentations Feb 12

  • Andrey: WoC
  • Daniel: Optimization

Presentations Feb 4

  • Shang: Deep learning for text

Recording of Jan 29 class

Due Mon Jan 14: Github Pull Request

  1. Sign up for GitHub if not already signed up. Pick default (free plan).
  2. Fork eveng/students - Start by forking the students repository
  3. [Clone][ref-clone] the repository to your computer (git clone https://github.com/yourGHid/students)
  4. Introduce yourself via a netid.md file (do not create netid.md, but replace netid by your own netid in all lowercase). Please provide at least one sentence on your background and one full paragraph explaining ether a project you are already working on or a project you'd like to work on for this class.
  5. git add netid.md
  6. git commit -m 'adding my background information': You may be asked to provide your email and name for the git client if you have not used git before
  7. git push
  8. Now go to your fork (https://github.com/yourGHid/students) and click on Create Pull Request on students repository

Syllabus and News for CS594/690: Evidence Engineering

  • Course: [COSCS-594/690]
  • ** MK623 TR 9:40AM-10:55AM (Min Kao 623) **
  • Instructor: Audris Mockus, audris@utk.edu office hours MK613 - on request

The primary purpose of the course is to learn-by-doing advanced operational data techniques including:

  1. Text analysis, e.g., Word2Vec, GloVe, NMF, LDA, LSTM
  2. Image analysis, e.g., RCNN, Mask-RCNN, CAM, ...
  3. Network analysis, network databases (neo4j),
  4. Advanced data analysis, Graphical models

Each of the techniques will be learned through work on a real project.

Draft Syllabus

About

Syllabus and news for CS594/690: Evidence Engineering

Resources

Readme
Activity
Custom properties

Stars

0 stars

Watchers

2 watching

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2 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Useful info:
Presentation on Apr 30 (starting at 8:30)
  • Applying text mining to detect opinions on climate change: Maria and Emily
  • Statistical analysis and some machine learning applications on interpreting Ribisome footprint data : Lu
  • Wrapping back on the World of Code and Developer Profiles: Andrey Karnauch
  • Daniel
Presentation on Apr 23
  • Self-organizing maps and applications in geospatial data: Linsey
  • Image Processing: Ethan
Presentation Apr 16
  • Tanner: D3
Presentation Apr 9
  • Guest presentation by Eduardo: computational aspects of Doc2Vec
Presentations Apr 2
  • Jerry: Chaos Monkey
  • Self Driving Cars: Shang
Presentations March 26
  • Evan: Recommender systems
  • Trish: Known Unknowns - testing recommender systems (tentative)
Presentations March 12
  • Dustin: SVM & SVR
  • Emily: text feature representation (tentative)
  • recording
Presentations March 5
Presentations Feb 26
  • Dakota: Security
Presentations Feb 12
  • Andrey: WoC
  • Daniel: Optimization
Presentations Feb 4
  • Shang: Deep learning for text
Recording of Jan 29 class
Due Mon Jan 14: Github Pull Request
  1. Sign up for GitHub if not already signed
up. Pick default (free plan).
  2. Fork eveng/students
- Start by forking the students repository
  3. [Clone][ref-clone] the repository to your computer (git clone https://github.com/yourGHid/students)
  4. Introduce yourself via a netid.md file (do not create netid.md, but replace netid by your own netid in all lowercase). Please provide at least one sentence on your background and one full paragraph explaining ether a project you are already working on or a project you'd like to work on for this class.
  5. git add netid.md
  6. git commit -m 'adding my background information': You may be asked to provide your email and name for the git client if you have not used git before
  7. git push
  8. Now go to your fork (https://github.com/yourGHid/students) and click on Create Pull Request on students repository
Syllabus and News for CS594/690: Evidence Engineering
  • Course: [COSCS-594/690]
  • ** MK623 TR 9:40AM-10:55AM (Min Kao 623) **
  • Instructor: Audris Mockus, audris@utk.edu office hours MK613 - on request
The primary purpose of the course is to learn-by-doing advanced operational data techniques including:
  1. Text analysis, e.g., Word2Vec, GloVe, NMF, LDA, LSTM
  2. Image analysis, e.g., RCNN, Mask-RCNN, CAM, ...
  3. Network analysis, network databases (neo4j),
  4. Advanced data analysis, Graphical models
Each of the techniques will be learned through work on a real project.
Draft Syllabus
About
Syllabus and news for CS594/690: Evidence Engineering
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
2 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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37 Commits
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ee.pdf
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Repository files navigation
Useful info:
Presentation on Apr 30 (starting at 8:30)
  • Applying text mining to detect opinions on climate change: Maria and Emily
  • Statistical analysis and some machine learning applications on interpreting Ribisome footprint data : Lu
  • Wrapping back on the World of Code and Developer Profiles: Andrey Karnauch
  • Daniel
Presentation on Apr 23
  • Self-organizing maps and applications in geospatial data: Linsey
  • Image Processing: Ethan
Presentation Apr 16
  • Tanner: D3
Presentation Apr 9
  • Guest presentation by Eduardo: computational aspects of Doc2Vec
Presentations Apr 2
  • Jerry: Chaos Monkey
  • Self Driving Cars: Shang
Presentations March 26
  • Evan: Recommender systems
  • Trish: Known Unknowns - testing recommender systems (tentative)
Presentations March 12
  • Dustin: SVM & SVR
  • Emily: text feature representation (tentative)
  • recording
Presentations March 5
Presentations Feb 26
  • Dakota: Security
Presentations Feb 12
  • Andrey: WoC
  • Daniel: Optimization
Presentations Feb 4
  • Shang: Deep learning for text
Recording of Jan 29 class
Due Mon Jan 14: Github Pull Request
  1. Sign up for GitHub if not already signed
up. Pick default (free plan).
  2. Fork eveng/students
- Start by forking the students repository
  3. [Clone][ref-clone] the repository to your computer (git clone https://github.com/yourGHid/students)
  4. Introduce yourself via a netid.md file (do not create netid.md, but replace netid by your own netid in all lowercase). Please provide at least one sentence on your background and one full paragraph explaining ether a project you are already working on or a project you'd like to work on for this class.
  5. git add netid.md
  6. git commit -m 'adding my background information': You may be asked to provide your email and name for the git client if you have not used git before
  7. git push
  8. Now go to your fork (https://github.com/yourGHid/students) and click on Create Pull Request on students repository
Syllabus and News for CS594/690: Evidence Engineering
  • Course: [COSCS-594/690]
  • ** MK623 TR 9:40AM-10:55AM (Min Kao 623) **
  • Instructor: Audris Mockus, audris@utk.edu office hours MK613 - on request
The primary purpose of the course is to learn-by-doing advanced operational data techniques including:
  1. Text analysis, e.g., Word2Vec, GloVe, NMF, LDA, LSTM
  2. Image analysis, e.g., RCNN, Mask-RCNN, CAM, ...
  3. Network analysis, network databases (neo4j),
  4. Advanced data analysis, Graphical models
Each of the techniques will be learned through work on a real project.
Draft Syllabus
About
Syllabus and news for CS594/690: Evidence Engineering
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
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highlight(document.body);
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mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
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observer.observe(document.body, { childList: true, subtree: true });
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}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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37 Commits
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README.md
README.md
ee.pdf
ee.pdf
Repository files navigation
Useful info:
Presentation on Apr 30 (starting at 8:30)
  • Applying text mining to detect opinions on climate change: Maria and Emily
  • Statistical analysis and some machine learning applications on interpreting Ribisome footprint data : Lu
  • Wrapping back on the World of Code and Developer Profiles: Andrey Karnauch
  • Daniel
Presentation on Apr 23
  • Self-organizing maps and applications in geospatial data: Linsey
  • Image Processing: Ethan
Presentation Apr 16
  • Tanner: D3
Presentation Apr 9
  • Guest presentation by Eduardo: computational aspects of Doc2Vec
Presentations Apr 2
  • Jerry: Chaos Monkey
  • Self Driving Cars: Shang
Presentations March 26
  • Evan: Recommender systems
  • Trish: Known Unknowns - testing recommender systems (tentative)
Presentations March 12
  • Dustin: SVM & SVR
  • Emily: text feature representation (tentative)
  • recording
Presentations March 5
Presentations Feb 26
  • Dakota: Security
Presentations Feb 12
  • Andrey: WoC
  • Daniel: Optimization
Presentations Feb 4
  • Shang: Deep learning for text
Recording of Jan 29 class
Due Mon Jan 14: Github Pull Request
  1. Sign up for GitHub if not already signed
up. Pick default (free plan).
  2. Fork eveng/students
- Start by forking the students repository
  3. [Clone][ref-clone] the repository to your computer (git clone https://github.com/yourGHid/students)
  4. Introduce yourself via a netid.md file (do not create netid.md, but replace netid by your own netid in all lowercase). Please provide at least one sentence on your background and one full paragraph explaining ether a project you are already working on or a project you'd like to work on for this class.
  5. git add netid.md
  6. git commit -m 'adding my background information': You may be asked to provide your email and name for the git client if you have not used git before
  7. git push
  8. Now go to your fork (https://github.com/yourGHid/students) and click on Create Pull Request on students repository
Syllabus and News for CS594/690: Evidence Engineering
  • Course: [COSCS-594/690]
  • ** MK623 TR 9:40AM-10:55AM (Min Kao 623) **
  • Instructor: Audris Mockus, audris@utk.edu office hours MK613 - on request
The primary purpose of the course is to learn-by-doing advanced operational data techniques including:
  1. Text analysis, e.g., Word2Vec, GloVe, NMF, LDA, LSTM
  2. Image analysis, e.g., RCNN, Mask-RCNN, CAM, ...
  3. Network analysis, network databases (neo4j),
  4. Advanced data analysis, Graphical models
Each of the techniques will be learned through work on a real project.
Draft Syllabus
About
Syllabus and news for CS594/690: Evidence Engineering
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
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return changed ? u.toString() : url;
} catch (e) {
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document.querySelectorAll('a[href]').forEach(function(a) {
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cleanLinks();
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mutations.forEach(function(m) {
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if (node.nodeType === 1) {
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var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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ee.pdf
ee.pdf
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Useful info:
Presentation on Apr 30 (starting at 8:30)
  • Applying text mining to detect opinions on climate change: Maria and Emily
  • Statistical analysis and some machine learning applications on interpreting Ribisome footprint data : Lu
  • Wrapping back on the World of Code and Developer Profiles: Andrey Karnauch
  • Daniel
Presentation on Apr 23
  • Self-organizing maps and applications in geospatial data: Linsey
  • Image Processing: Ethan
Presentation Apr 16
  • Tanner: D3
Presentation Apr 9
  • Guest presentation by Eduardo: computational aspects of Doc2Vec
Presentations Apr 2
  • Jerry: Chaos Monkey
  • Self Driving Cars: Shang
Presentations March 26
  • Evan: Recommender systems
  • Trish: Known Unknowns - testing recommender systems (tentative)
Presentations March 12
  • Dustin: SVM & SVR
  • Emily: text feature representation (tentative)
  • recording
Presentations March 5
Presentations Feb 26
  • Dakota: Security
Presentations Feb 12
  • Andrey: WoC
  • Daniel: Optimization
Presentations Feb 4
  • Shang: Deep learning for text
Recording of Jan 29 class
Due Mon Jan 14: Github Pull Request
  1. Sign up for GitHub if not already signed
up. Pick default (free plan).
  2. Fork eveng/students
- Start by forking the students repository
  3. [Clone][ref-clone] the repository to your computer (git clone https://github.com/yourGHid/students)
  4. Introduce yourself via a netid.md file (do not create netid.md, but replace netid by your own netid in all lowercase). Please provide at least one sentence on your background and one full paragraph explaining ether a project you are already working on or a project you'd like to work on for this class.
  5. git add netid.md
  6. git commit -m 'adding my background information': You may be asked to provide your email and name for the git client if you have not used git before
  7. git push
  8. Now go to your fork (https://github.com/yourGHid/students) and click on Create Pull Request on students repository
Syllabus and News for CS594/690: Evidence Engineering
  • Course: [COSCS-594/690]
  • ** MK623 TR 9:40AM-10:55AM (Min Kao 623) **
  • Instructor: Audris Mockus, audris@utk.edu office hours MK613 - on request
The primary purpose of the course is to learn-by-doing advanced operational data techniques including:
  1. Text analysis, e.g., Word2Vec, GloVe, NMF, LDA, LSTM
  2. Image analysis, e.g., RCNN, Mask-RCNN, CAM, ...
  3. Network analysis, network databases (neo4j),
  4. Advanced data analysis, Graphical models
Each of the techniques will be learned through work on a real project.
Draft Syllabus
About
Syllabus and news for CS594/690: Evidence Engineering
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
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// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
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observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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BranchesTags
Latest commit
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37 Commits
Folders and files
NameName
Last commit message
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README.md
README.md
ee.pdf
ee.pdf
Repository files navigation
Useful info:
Presentation on Apr 30 (starting at 8:30)
  • Applying text mining to detect opinions on climate change: Maria and Emily
  • Statistical analysis and some machine learning applications on interpreting Ribisome footprint data : Lu
  • Wrapping back on the World of Code and Developer Profiles: Andrey Karnauch
  • Daniel
Presentation on Apr 23
  • Self-organizing maps and applications in geospatial data: Linsey
  • Image Processing: Ethan
Presentation Apr 16
  • Tanner: D3
Presentation Apr 9
  • Guest presentation by Eduardo: computational aspects of Doc2Vec
Presentations Apr 2
  • Jerry: Chaos Monkey
  • Self Driving Cars: Shang
Presentations March 26
  • Evan: Recommender systems
  • Trish: Known Unknowns - testing recommender systems (tentative)
Presentations March 12
  • Dustin: SVM & SVR
  • Emily: text feature representation (tentative)
  • recording
Presentations March 5
Presentations Feb 26
  • Dakota: Security
Presentations Feb 12
  • Andrey: WoC
  • Daniel: Optimization
Presentations Feb 4
  • Shang: Deep learning for text
Recording of Jan 29 class
Due Mon Jan 14: Github Pull Request
  1. Sign up for GitHub if not already signed
up. Pick default (free plan).
  2. Fork eveng/students
- Start by forking the students repository
  3. [Clone][ref-clone] the repository to your computer (git clone https://github.com/yourGHid/students)
  4. Introduce yourself via a netid.md file (do not create netid.md, but replace netid by your own netid in all lowercase). Please provide at least one sentence on your background and one full paragraph explaining ether a project you are already working on or a project you'd like to work on for this class.
  5. git add netid.md
  6. git commit -m 'adding my background information': You may be asked to provide your email and name for the git client if you have not used git before
  7. git push
  8. Now go to your fork (https://github.com/yourGHid/students) and click on Create Pull Request on students repository
Syllabus and News for CS594/690: Evidence Engineering
  • Course: [COSCS-594/690]
  • ** MK623 TR 9:40AM-10:55AM (Min Kao 623) **
  • Instructor: Audris Mockus, audris@utk.edu office hours MK613 - on request
The primary purpose of the course is to learn-by-doing advanced operational data techniques including:
  1. Text analysis, e.g., Word2Vec, GloVe, NMF, LDA, LSTM
  2. Image analysis, e.g., RCNN, Mask-RCNN, CAM, ...
  3. Network analysis, network databases (neo4j),
  4. Advanced data analysis, Graphical models
Each of the techniques will be learned through work on a real project.
Draft Syllabus
About
Syllabus and news for CS594/690: Evidence Engineering
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
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observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Useful info:
Presentation on Apr 30 (starting at 8:30)
  • Applying text mining to detect opinions on climate change: Maria and Emily
  • Statistical analysis and some machine learning applications on interpreting Ribisome footprint data : Lu
  • Wrapping back on the World of Code and Developer Profiles: Andrey Karnauch
  • Daniel
Presentation on Apr 23
  • Self-organizing maps and applications in geospatial data: Linsey
  • Image Processing: Ethan
Presentation Apr 16
  • Tanner: D3
Presentation Apr 9
  • Guest presentation by Eduardo: computational aspects of Doc2Vec
Presentations Apr 2
  • Jerry: Chaos Monkey
  • Self Driving Cars: Shang
Presentations March 26
  • Evan: Recommender systems
  • Trish: Known Unknowns - testing recommender systems (tentative)
Presentations March 12
  • Dustin: SVM & SVR
  • Emily: text feature representation (tentative)
  • recording
Presentations March 5
Presentations Feb 26
  • Dakota: Security
Presentations Feb 12
  • Andrey: WoC
  • Daniel: Optimization
Presentations Feb 4
  • Shang: Deep learning for text
Recording of Jan 29 class
Due Mon Jan 14: Github Pull Request
  1. Sign up for GitHub if not already signed
up. Pick default (free plan).
  2. Fork eveng/students
- Start by forking the students repository
  3. [Clone][ref-clone] the repository to your computer (git clone https://github.com/yourGHid/students)
  4. Introduce yourself via a netid.md file (do not create netid.md, but replace netid by your own netid in all lowercase). Please provide at least one sentence on your background and one full paragraph explaining ether a project you are already working on or a project you'd like to work on for this class.
  5. git add netid.md
  6. git commit -m 'adding my background information': You may be asked to provide your email and name for the git client if you have not used git before
  7. git push
  8. Now go to your fork (https://github.com/yourGHid/students) and click on Create Pull Request on students repository
Syllabus and News for CS594/690: Evidence Engineering
  • Course: [COSCS-594/690]
  • ** MK623 TR 9:40AM-10:55AM (Min Kao 623) **
  • Instructor: Audris Mockus, audris@utk.edu office hours MK613 - on request
The primary purpose of the course is to learn-by-doing advanced operational data techniques including:
  1. Text analysis, e.g., Word2Vec, GloVe, NMF, LDA, LSTM
  2. Image analysis, e.g., RCNN, Mask-RCNN, CAM, ...
  3. Network analysis, network databases (neo4j),
  4. Advanced data analysis, Graphical models
Each of the techniques will be learned through work on a real project.
Draft Syllabus
About
Syllabus and news for CS594/690: Evidence Engineering
Resources
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::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
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console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
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37 Commits
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README.md
README.md
ee.pdf
ee.pdf
Repository files navigation
Useful info:
Presentation on Apr 30 (starting at 8:30)
  • Applying text mining to detect opinions on climate change: Maria and Emily
  • Statistical analysis and some machine learning applications on interpreting Ribisome footprint data : Lu
  • Wrapping back on the World of Code and Developer Profiles: Andrey Karnauch
  • Daniel
Presentation on Apr 23
  • Self-organizing maps and applications in geospatial data: Linsey
  • Image Processing: Ethan
Presentation Apr 16
  • Tanner: D3
Presentation Apr 9
  • Guest presentation by Eduardo: computational aspects of Doc2Vec
Presentations Apr 2
  • Jerry: Chaos Monkey
  • Self Driving Cars: Shang
Presentations March 26
  • Evan: Recommender systems
  • Trish: Known Unknowns - testing recommender systems (tentative)
Presentations March 12
  • Dustin: SVM & SVR
  • Emily: text feature representation (tentative)
  • recording
Presentations March 5
Presentations Feb 26
  • Dakota: Security
Presentations Feb 12
  • Andrey: WoC
  • Daniel: Optimization
Presentations Feb 4
  • Shang: Deep learning for text
Recording of Jan 29 class
Due Mon Jan 14: Github Pull Request
  1. Sign up for GitHub if not already signed
up. Pick default (free plan).
  2. Fork eveng/students
- Start by forking the students repository
  3. [Clone][ref-clone] the repository to your computer (git clone https://github.com/yourGHid/students)
  4. Introduce yourself via a netid.md file (do not create netid.md, but replace netid by your own netid in all lowercase). Please provide at least one sentence on your background and one full paragraph explaining ether a project you are already working on or a project you'd like to work on for this class.
  5. git add netid.md
  6. git commit -m 'adding my background information': You may be asked to provide your email and name for the git client if you have not used git before
  7. git push
  8. Now go to your fork (https://github.com/yourGHid/students) and click on Create Pull Request on students repository
Syllabus and News for CS594/690: Evidence Engineering
  • Course: [COSCS-594/690]
  • ** MK623 TR 9:40AM-10:55AM (Min Kao 623) **
  • Instructor: Audris Mockus, audris@utk.edu office hours MK613 - on request
The primary purpose of the course is to learn-by-doing advanced operational data techniques including:
  1. Text analysis, e.g., Word2Vec, GloVe, NMF, LDA, LSTM
  2. Image analysis, e.g., RCNN, Mask-RCNN, CAM, ...
  3. Network analysis, network databases (neo4j),
  4. Advanced data analysis, Graphical models
Each of the techniques will be learned through work on a real project.
Draft Syllabus
About
Syllabus and news for CS594/690: Evidence Engineering
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors