diff --git a/courses/CSCI-4140.html b/courses/CSCI-4140.html new file mode 100644 index 000000000..280b82932 --- /dev/null +++ b/courses/CSCI-4140.html @@ -0,0 +1,369 @@ + + + + + CSCI-4140: Machine Learning & Optimiztn + + + + + + + + + + + + + + + + +
+ +
+ +
+
+
+
+

+ Machine Learning & Optimiztn +

+

+ CSCI-4140 +

+

+ The first portion of this course introduces the optimization background necessary to understand the algorithms that dominate the landscape of machine learning. The second portion introduces effective architectures used in modern machine learning. Students revisit classical models and learn state-of-the-art models, always in service of gaining algorithmic insight that is broadly useful beyond specific models. +

+
+ + ? credits + +
+
+
+ Prereqs: +
+
+ + none + +
+
+
+
+

+ Past Term Data +

+ + +
+
+
+ + + + Offered +
+
+ + + + Not Offered +
+
+ + + + Offered as Cross-Listing Only +
+
+ + + + No Term Data +
+
+
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SpringSummerFall
(Session 1)(Session 2)
2024 + + +
2023 + + +
2022 + + +
2021 + + +
2020 + + +
2019 + + +
2018 + + +
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+
+
+ + diff --git a/courses/CSCI-4160.html b/courses/CSCI-4160.html new file mode 100644 index 000000000..c83e57e47 --- /dev/null +++ b/courses/CSCI-4160.html @@ -0,0 +1,369 @@ + + + + + CSCI-4160: Reinforcement Learning + + + + + + + + + + + + + + + + +
+ +
+ +
+
+
+
+

+ Reinforcement Learning +

+

+ CSCI-4160 +

+

+ This is an introductory course on the theory and practice of reinforcement learning (RL). We will derive the full RL framework, starting from Markov chains and Markov reward processes and building up to Markov decision processes. We will then cover classic RL approaches such as dynamic programming, Monte Carlo methods and Q-learning. Furthermore, we will cover more advanced topics such as deep learning, deep RL, as well as policy-gradient and actor-critic methods. Course activities include programming assignments as well as written homework testing students’ understanding of the material. +

+
+ + ? credits + +
+
+
+ Prereqs: +
+
+ + none + +
+
+
+
+

+ Past Term Data +

+ + +
+
+
+ + + + Offered +
+
+ + + + Not Offered +
+
+ + + + Offered as Cross-Listing Only +
+
+ + + + No Term Data +
+
+
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SpringSummerFall
(Session 1)(Session 2)
2024 + + +
2023 + + +
2022 + + +
2021 + + +
2020 + + +
2019 + + +
2018 + + +
2017 + + +
2016 + + +
2015 + + +
2014 + + +
2013 + + +
2012 + + +
2011 + + +
2010 + + +
2009 + + +
2008 + + +
2007 + + +
2006 + + +
2005 + + +
2004 + + +
2003 + + +
2002 + + +
2001 + + +
2000 + + +
1999 + + +
1998 + + +
+
+
+ + diff --git a/courses/CSCI-4170.html b/courses/CSCI-4170.html new file mode 100644 index 000000000..b9f38b41c --- /dev/null +++ b/courses/CSCI-4170.html @@ -0,0 +1,369 @@ + + + + + CSCI-4170: Projects In Ai & Machine Lrng + + + + + + + + + + + + + + + + +
+ +
+ +
+
+
+
+

+ Projects In Ai & Machine Lrng +

+

+ CSCI-4170 +

+

+ We will study machine learning and AI solutions to real world problems using publicly available datasets. Topics include Deep Learning, Training Neural Networks (NN), Recurrent NN, Convolution NN, Auto-encoders, Generative Models, Natural Language processing (NLP), Reinforcement Learning, Diffusion models, Recommender Systems. +

+
+ + ? credits + +
+
+
+ Prereqs: +
+
+ + none + +
+
+
+
+

+ Past Term Data +

+ + +
+
+
+ + + + Offered +
+
+ + + + Not Offered +
+
+ + + + Offered as Cross-Listing Only +
+
+ + + + No Term Data +
+
+
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SpringSummerFall
(Session 1)(Session 2)
2024 + + +
2023 + + +
2022 + + +
2021 + + +
2020 + + +
2019 + + +
2018 + + +
2017 + + +
2016 + + +
2015 + + +
2014 + + +
2013 + + +
2012 + + +
2011 + + +
2010 + + +
2009 + + +
2008 + + +
2007 + + +
2006 + + +
2005 + + +
2004 + + +
2003 + + +
2002 + + +
2001 + + +
2000 + + +
1999 + + +
1998 + + +
+
+
+ + diff --git a/courses/CSCI-4180.html b/courses/CSCI-4180.html new file mode 100644 index 000000000..e6718da60 --- /dev/null +++ b/courses/CSCI-4180.html @@ -0,0 +1,369 @@ + + + + + CSCI-4180: Trustworthy Machine Learning + + + + + + + + + + + + + + + + +
+ +
+ +
+
+
+
+

+ Trustworthy Machine Learning +

+

+ CSCI-4180 +

+

+ This seminar course introduces students with knowledge of machine learning to modern desiderata for trustworthy machine learning, including alignment, fairness, adversarial robustness, privacy, and their interrelations. Students read, present, and discuss seminal and influential recent papers in the field. The course includes a project component aimed at synthesizing the students’ learning. +

+
+ + ? credits + +
+
+
+ Prereqs: +
+
+ + none + +
+
+
+
+

+ Past Term Data +

+ + +
+
+
+ + + + Offered +
+
+ + + + Not Offered +
+
+ + + + Offered as Cross-Listing Only +
+
+ + + + No Term Data +
+
+
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SpringSummerFall
(Session 1)(Session 2)
2024 + + +
2023 + + +
2022 + + +
2021 + + +
2020 + + +
2019 + + +
2018 + + +
2017 + + +
2016 + + +
2015 + + +
2014 + + +
2013 + + +
2012 + + +
2011 + + +
2010 + + +
2009 + + +
2008 + + +
2007 + + +
2006 + + +
2005 + + +
2004 + + +
2003 + + +
2002 + + +
2001 + + +
2000 + + +
1999 + + +
1998 + + +
+
+
+ + diff --git a/courses/PHYS-2160.html b/courses/PHYS-2160.html new file mode 100644 index 000000000..4d32aa026 --- /dev/null +++ b/courses/PHYS-2160.html @@ -0,0 +1,369 @@ + + + + + PHYS-2160: Mentor First-year Phys Stdnts + + + + + + + + + + + + + + + + +
+ +
+ +
+
+
+
+

+ Mentor First-year Phys Stdnts +

+

+ PHYS-2160 +

+

+ Practicum in mentoring new students in Physics with focus on developing Mentor technical leadership skills. Note that this course cannot be applied toward the satisfaction of the Institute Math/Science Core requirement. +

+
+ + ? credits + +
+
+
+ Prereqs: +
+
+ + none + +
+
+
+
+

+ Past Term Data +

+ + +
+
+
+ + + + Offered +
+
+ + + + Not Offered +
+
+ + + + Offered as Cross-Listing Only +
+
+ + + + No Term Data +
+
+
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SpringSummerFall
(Session 1)(Session 2)
2024 + + +
2023 + + +
2022 + + +
2021 + + +
2020 + + +
2019 + + +
2018 + + +
2017 + + +
2016 + + +
2015 + + +
2014 + + +
2013 + + +
2012 + + +
2011 + + +
2010 + + +
2009 + + +
2008 + + +
2007 + + +
2006 + + +
2005 + + +
2004 + + +
2003 + + +
2002 + + +
2001 + + +
2000 + + +
1999 + + +
1998 + + +
+
+
+ + diff --git a/json/courses_list.json b/json/courses_list.json index 059277197..7964c899a 100644 --- a/json/courses_list.json +++ b/json/courses_list.json @@ -2096,7 +2096,11 @@ "CSCI-4110", "CSCI-4120", "CSCI-4130", + "CSCI-4140", "CSCI-4150", + "CSCI-4160", + "CSCI-4170", + "CSCI-4180", "CSCI-4190", "CSCI-4210", "CSCI-4220", @@ -5313,6 +5317,7 @@ "PHYS-2050", "PHYS-2100", "PHYS-2110", + "PHYS-2160", "PHYS-2210", "PHYS-2220", "PHYS-2310", diff --git a/json/searchable_catalog.json b/json/searchable_catalog.json index 804575d4b..737ea439a 100644 --- a/json/searchable_catalog.json +++ b/json/searchable_catalog.json @@ -14933,6 +14933,13 @@ "description" : "This interdisciplinary course explores the fascinating intersection between Artificial Intelligence (AI) as portrayed in fiction and its real-world counterparts. Students will delve into literary and cinematic works as well as news media and current affairs that feature AI while concurrently studying the historical development, technological underpinnings, ethical considerations, and societal impacts of AI. Through critical analysis, discussions, and a project, students will gain a nuanced understanding of AI's portrayal in various media and its implications in our rapidly evolving world.", "name" : "Ai In Fiction And Fact" }, + { + "attributes" : null, + "code" : "CSCI-4140", + "credits" : "? credits", + "description" : "The first portion of this course introduces the optimization background necessary to understand the algorithms that dominate the landscape of machine learning. The second portion introduces effective architectures used in modern machine learning. Students revisit classical models and learn state-of-the-art models, always in service of gaining algorithmic insight that is broadly useful beyond specific models.", + "name" : "Machine Learning & Optimiztn" + }, { "attributes" : null, "code" : "CSCI-4150", @@ -14940,6 +14947,27 @@ "description" : "Topics and techniques of artificial intelligence using the language LISP. Topics include search, knowledge representation, expert systems, theorem proving, natural language interfaces, learning, game playing, and computer vision. Techniques include pattern matching, data-driven programming, substitution rules, frames, heuristic search, transition networks, neural networks, and evolutionary computation. Development of programming proficiency in LISP is emphasized.", "name" : "Introduction To Artificial Intelligence" }, + { + "attributes" : null, + "code" : "CSCI-4160", + "credits" : "? credits", + "description" : "This is an introductory course on the theory and practice of reinforcement learning (RL). We will derive the full RL framework, starting from Markov chains and Markov reward processes and building up to Markov decision processes. We will then cover classic RL approaches such as dynamic programming, Monte Carlo methods and Q-learning. Furthermore, we will cover more advanced topics such as deep learning, deep RL, as well as policy-gradient and actor-critic methods. Course activities include programming assignments as well as written homework testing students\u2019 understanding of the material.", + "name" : "Reinforcement Learning" + }, + { + "attributes" : null, + "code" : "CSCI-4170", + "credits" : "? credits", + "description" : "We will study machine learning and AI solutions to real world problems using publicly available datasets. Topics include Deep Learning, Training Neural Networks (NN), Recurrent NN, Convolution NN, Auto-encoders, Generative Models, Natural Language processing (NLP), Reinforcement Learning, Diffusion models, Recommender Systems.", + "name" : "Projects In Ai & Machine Lrng" + }, + { + "attributes" : null, + "code" : "CSCI-4180", + "credits" : "? credits", + "description" : "This seminar course introduces students with knowledge of machine learning to modern desiderata for trustworthy machine learning, including alignment, fairness, adversarial robustness, privacy, and their interrelations. Students read, present, and discuss seminal and influential recent papers in the field. The course includes a project component aimed at synthesizing the students\u2019 learning.", + "name" : "Trustworthy Machine Learning" + }, { "attributes" : null, "code" : "CSCI-4190", @@ -37996,6 +38024,13 @@ "description" : "This course is not in the most recent catalog. It may have been discontinued, had its course code changed, or just not be in the catalog for some other reason.", "name" : "Modern Physics" }, + { + "attributes" : null, + "code" : "PHYS-2160", + "credits" : "? credits", + "description" : "Practicum in mentoring new students in Physics with focus on developing Mentor technical leadership skills. Note that this course cannot be applied toward the satisfaction of the Institute Math/Science Core requirement.", + "name" : "Mentor First-year Phys Stdnts" + }, { "attributes" : null, "code" : "PHYS-2210",