From 827b448546df616ffef6907587ceba9eb4cbb73e Mon Sep 17 00:00:00 2001
From: Soma Dhavala <soma.dhavala@gmail.com>
Date: Tue, 31 Dec 2024 09:49:10 +0530
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll             | 2 +-
 index.html            | 9 ++++++++-
 lectures/w16-l01.html | 1 +
 search.json           | 6 +++---
 sitemap.xml           | 4 ++--
 5 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/.nojekyll b/.nojekyll
index b4a7241..1cb77ea 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-422bae10
\ No newline at end of file
+19902647
\ No newline at end of file
diff --git a/index.html b/index.html
index 87bb6e3..90ac338 100644
--- a/index.html
+++ b/index.html
@@ -424,6 +424,13 @@ <h1 class="unnumbered">Welcome</h1><p><img src="crisp-ml-process.jpg" title="AI-
 <section id="announcements" class="level2">
 <h2 class="anchored" data-anchor-id="announcements">Announcements</h2>
 <ul>
+<li>[30-November-2024] COURSE ENDS</li>
+<li>[30-November-2024] Project Presentations</li>
+<li>[29-November-2024] Project Presentations</li>
+<li>[26-November-2024] Machine Unlearning <a href="./lectures/w16-l02.html">W16-L02</a> added to course page</li>
+<li>[26-November-2024] Fairness and Bias <a href="./lectures/w16-l01.html">W16-L01</a> added to course page</li>
+<li>[19-November-2024] Meta Learning lecture page <a href="./lectures/w15-l01.html">W15-L01</a> added to course page</li>
+<li>[15-November-2024] Data Labelling guest lecture deck added to course page</li>
 <li>[13-November-2024] AI Security guest lecture deck added to course page</li>
 <li>[07-November-2024] Lecture page added to <a href="./lectures/w13-l01.html">W13-L01</a>, <a href="./lectures/w13-l02.html">W13-L02</a></li>
 <li>[25-October-2024] Lecture page added to <a href="./lectures/w11-l02.html">W11-L02</a></li>
@@ -481,7 +488,7 @@ <h2 class="anchored" data-anchor-id="overview">Overview</h2>
 <li>incorporate principles from Responsible AI and build ML systems which can consist of many models and tools.</li>
 </ul></li>
 </ul>
-<p><strong>Part-3: Intro to LLMOps &amp; Application</strong></p>
+<p><strong>Part-3: Intro to LLM(Ops) &amp; Application</strong></p>
 <ul>
 <li>Topics
 <ul>
diff --git a/lectures/w16-l01.html b/lectures/w16-l01.html
index e3e38bd..1ea92dc 100644
--- a/lectures/w16-l01.html
+++ b/lectures/w16-l01.html
@@ -440,6 +440,7 @@ <h3 class="anchored" data-anchor-id="post-class">Post-Class</h3>
 <li><a href="https://arxiv.org/abs/2304.07683">paper</a> Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies</li>
 <li><a href="https://arxiv.org/abs/2403.14636">paper</a> AI Fairness in Practice</li>
 <li><a href="https://fairmlbook.org/">book</a> Fair ML Book</li>
+<li><a href="https://arxiv.org/abs/1908.08843">paper</a> Fairness in Deep Learning: A Computational Perspective</li>
 </ol>
 
 
diff --git a/search.json b/search.json
index ce413a7..c8ea943 100644
--- a/search.json
+++ b/search.json
@@ -223,7 +223,7 @@
     "href": "index.html#announcements",
     "title": "AI-839",
     "section": "Announcements",
-    "text": "Announcements\n\n[13-November-2024] AI Security guest lecture deck added to course page\n[07-November-2024] Lecture page added to W13-L01, W13-L02\n[25-October-2024] Lecture page added to W11-L02\n[22-October-2024] Dr. Amit’s Causal ML deck added to course page.\n[22-October-2024] Guest lecture decks added.\n[01-October-2024] Generic feedback on HWs is added to homeworks page.\n[30-September-2024] Midterm Bonus Problem released. Due by 11.59pm, Oct 15, 2024 IST.\n[27-September-2024] Midterm at 11.15am, Tue, Sept, 2024 IST.\n[17-September-2024] HW-05 extended to 11.59pm, Tue, Sept, 2024 IST.\n[16-September-2024] Notes added to W08-L01. Updated course page with scheudle for the rest of the course (tentative).\n[12-September-2024] Notes added to W07-L01. W07-L02 is added.\n[05-September-2024] Homework, Minor and Major preoject details added. See HW-05, HW-06, Minor Project, Major Project for details. Course pages updated.\n[05-September-2024] Lecture Page W05-L01, W05-L02, W06-L01, W06-L02 added. Course pages updated.\n[25-August-2024] Lecture Page W03-L02, W04-L01, W04-L02 added. Course pages updated.\n[23-August-2024] HW-03 and HW-04 added.\n[13-August-2024] Lecture Page W03-L01 added. Course pages updated.\n[09-August-2024] Lecture Page W02-L02 added. Course pages updated. HW-02 added\n[06-August-2024] Lecture Page W02-L01 added. Project Card, as a jupyter notebook is added.\n[01-August-2024] Course website up, Lecture Page W01-L01 added. HW-01 added.",
+    "text": "Announcements\n\n[30-November-2024] COURSE ENDS\n[30-November-2024] Project Presentations\n[29-November-2024] Project Presentations\n[26-November-2024] Machine Unlearning W16-L02 added to course page\n[26-November-2024] Fairness and Bias W16-L01 added to course page\n[19-November-2024] Meta Learning lecture page W15-L01 added to course page\n[15-November-2024] Data Labelling guest lecture deck added to course page\n[13-November-2024] AI Security guest lecture deck added to course page\n[07-November-2024] Lecture page added to W13-L01, W13-L02\n[25-October-2024] Lecture page added to W11-L02\n[22-October-2024] Dr. Amit’s Causal ML deck added to course page.\n[22-October-2024] Guest lecture decks added.\n[01-October-2024] Generic feedback on HWs is added to homeworks page.\n[30-September-2024] Midterm Bonus Problem released. Due by 11.59pm, Oct 15, 2024 IST.\n[27-September-2024] Midterm at 11.15am, Tue, Sept, 2024 IST.\n[17-September-2024] HW-05 extended to 11.59pm, Tue, Sept, 2024 IST.\n[16-September-2024] Notes added to W08-L01. Updated course page with scheudle for the rest of the course (tentative).\n[12-September-2024] Notes added to W07-L01. W07-L02 is added.\n[05-September-2024] Homework, Minor and Major preoject details added. See HW-05, HW-06, Minor Project, Major Project for details. Course pages updated.\n[05-September-2024] Lecture Page W05-L01, W05-L02, W06-L01, W06-L02 added. Course pages updated.\n[25-August-2024] Lecture Page W03-L02, W04-L01, W04-L02 added. Course pages updated.\n[23-August-2024] HW-03 and HW-04 added.\n[13-August-2024] Lecture Page W03-L01 added. Course pages updated.\n[09-August-2024] Lecture Page W02-L02 added. Course pages updated. HW-02 added\n[06-August-2024] Lecture Page W02-L01 added. Project Card, as a jupyter notebook is added.\n[01-August-2024] Course website up, Lecture Page W01-L01 added. HW-01 added.",
     "crumbs": [
       "Welcome"
     ]
@@ -233,7 +233,7 @@
     "href": "index.html#overview",
     "title": "AI-839",
     "section": "Overview",
-    "text": "Overview\nPrereqs\n\nExposure and skill in data handling, building models in Python, PyTorch\nExposure and skill in developing code using Python, Git, IDEs like VS Code\nA foundation course in Machine Learning, Deep Learning, Data Modeling, working with (Big) Data\n\nPart-1: Essentials\n\nTopics\n\nbasic principles and MLOps with Open Source Software\nthree assignments\n\nLearning Outcomes: students will be able to\n\ndeploy models with logging, documentation, unit tests, and APIs\nunderstand a conceptual framework to approach MLOps holistically\n\n\nPart-2: Full Stack MLOps\n\nTopics\n\nholistic understanding of ML development, beyond chasing typical performance metrics\none assignment, one mini project and a midterm\n\nLearning Outcomes: students will be able to\n\ndeploy models, observe their performance, make improvements, redeploy them.\n\nensure that the ML pipeline is reproducible.\nincorporate principles from Responsible AI and build ML systems which can consist of many models and tools.\n\n\nPart-3: Intro to LLMOps & Application\n\nTopics\n\npractice, cloud solutions\ncapstone project and presentations\ninvited lectures from Industry\n\nLearning Outcomes: students will be able to\n\nframe, discover, develop, deploy, monitor, improve, re-deploy and maintain an ML Application\napproach the problem holistically, optimize RoI",
+    "text": "Overview\nPrereqs\n\nExposure and skill in data handling, building models in Python, PyTorch\nExposure and skill in developing code using Python, Git, IDEs like VS Code\nA foundation course in Machine Learning, Deep Learning, Data Modeling, working with (Big) Data\n\nPart-1: Essentials\n\nTopics\n\nbasic principles and MLOps with Open Source Software\nthree assignments\n\nLearning Outcomes: students will be able to\n\ndeploy models with logging, documentation, unit tests, and APIs\nunderstand a conceptual framework to approach MLOps holistically\n\n\nPart-2: Full Stack MLOps\n\nTopics\n\nholistic understanding of ML development, beyond chasing typical performance metrics\none assignment, one mini project and a midterm\n\nLearning Outcomes: students will be able to\n\ndeploy models, observe their performance, make improvements, redeploy them.\n\nensure that the ML pipeline is reproducible.\nincorporate principles from Responsible AI and build ML systems which can consist of many models and tools.\n\n\nPart-3: Intro to LLM(Ops) & Application\n\nTopics\n\npractice, cloud solutions\ncapstone project and presentations\ninvited lectures from Industry\n\nLearning Outcomes: students will be able to\n\nframe, discover, develop, deploy, monitor, improve, re-deploy and maintain an ML Application\napproach the problem holistically, optimize RoI",
     "crumbs": [
       "Welcome"
     ]
@@ -809,7 +809,7 @@
     "href": "lectures/w16-l01.html#materials",
     "title": "16A: Fairness & Bias",
     "section": "",
-    "text": "Pre-work:\n\n\nIn-Class\n\nAI Fairness 360 An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias\nnotebook Detecting and mitigating age bias in credit decisions\npaper AI Fairness: from Principles to Practice\n\n\n\nPost-Class\n\ncollection on Fairness in AI\npaper Towards Fairness Certification in AI\npaper Fairness in AI and Its Long-Term Implications on Society\npaper The Pursuit of Fairness in Artificial Intelligence Models: A Survey\npaper Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies\npaper AI Fairness in Practice\nbook Fair ML Book",
+    "text": "Pre-work:\n\n\nIn-Class\n\nAI Fairness 360 An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias\nnotebook Detecting and mitigating age bias in credit decisions\npaper AI Fairness: from Principles to Practice\n\n\n\nPost-Class\n\ncollection on Fairness in AI\npaper Towards Fairness Certification in AI\npaper Fairness in AI and Its Long-Term Implications on Society\npaper The Pursuit of Fairness in Artificial Intelligence Models: A Survey\npaper Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies\npaper AI Fairness in Practice\nbook Fair ML Book\npaper Fairness in Deep Learning: A Computational Perspective",
     "crumbs": [
       "Lectures[ML Science]",
       "16A: Fairness & Bias"
diff --git a/sitemap.xml b/sitemap.xml
index 50f53b8..d011e6e 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -18,7 +18,7 @@
   </url>
   <url>
     <loc>https://mlsquare.github.io/ai-839/index.html</loc>
-    <lastmod>2024-11-13T04:20:40.308Z</lastmod>
+    <lastmod>2024-12-31T04:16:26.140Z</lastmod>
   </url>
   <url>
     <loc>https://mlsquare.github.io/ai-839/preface.html</loc>
@@ -110,7 +110,7 @@
   </url>
   <url>
     <loc>https://mlsquare.github.io/ai-839/lectures/w16-l01.html</loc>
-    <lastmod>2024-11-25T13:45:50.036Z</lastmod>
+    <lastmod>2024-11-25T14:57:34.967Z</lastmod>
   </url>
   <url>
     <loc>https://mlsquare.github.io/ai-839/lectures/w16-l02.html</loc>