+ src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTWqFsXhSzRUBQ3g42BK553ZfJ7LjJhkc-AwPPFW22mOG67rm1snSvi-WvkM-jwdRa6M-t8cWWOMoJg/pubhtml?widget=true&headers=false">
From c4d8a28b3e94b6e88768c0dd373516441777016c Mon Sep 17 00:00:00 2001
From: Gabriel When contacting staff,
+ When contacting staff, the Ed forum is preferred. Please use email sparingly.
- CS 181 provides a broad and rigorous introduction to the principles of machine learning, probabilistic
+ CS 1810 provides a broad and rigorous introduction to the principles of machine learning, probabilistic
reasoning and decision making in uncertain environments. We will discuss the motivations behind common
machine learning algorithms, and the properties that determine whether or not they will work well for a
particular task. You will derive the mathematical underpinnings for many common methods, as well as apply
@@ -18,9 +18,9 @@
- Copyright © Harvard CS181
+ Copyright © Harvard CS1810
diff --git a/schedule.html b/schedule.html
index 62cf330..7fb38b1 100644
--- a/schedule.html
+++ b/schedule.html
@@ -8,7 +8,7 @@
\ No newline at end of file
diff --git a/syllabus.html b/syllabus.html
index 6bec568..e19af08 100644
--- a/syllabus.html
+++ b/syllabus.html
@@ -9,7 +9,7 @@
\ No newline at end of file
diff --git a/sections.html b/sections.html
index d400bf2..52860d3 100644
--- a/sections.html
+++ b/sections.html
@@ -18,13 +18,14 @@
-
\ No newline at end of file
diff --git a/staff.html b/staff.html
index f4a1fc0..f0528d3 100644
--- a/staff.html
+++ b/staff.html
@@ -24,7 +24,9 @@ Teaching Fellows
Contacting us
- Course Description
Course Description
directions.
- The goal of CS 181 is to combine mathematical derivation and coding assignments to provide a strong and + The goal of CS 1810 is to combine mathematical derivation and coding assignments to provide a strong and rigorous conceptual grounding in the principles of machine learning (e.g. being able to reason about how different methods should behave in different circumstances). Students interested primarily in theory may prefer Stat195, CS184, and other learning theory offerings. Students interested primarily in implementation @@ -43,8 +43,8 @@
Team - The CS181 team consists of the course instructors---Finale Doshi Velez and David Alvarez-Melis---as well as - a large staff of TFs lead by two co-head TFs---Charu Badrinath and Alex Cai. We are all dedicated to helping + The CS1810 team consists of the course instructors---Finale Doshi Velez and David Alvarez-Melis---as well as + a large staff of TFs lead by two co-head TFs---Sam Jones and Gabe Sun. We are all dedicated to helping you to learn the fundamentals of machine learning.
@@ -63,7 +63,7 @@Office Hours Most office hours will be in person; one set will be over zoom. See the schedule page for information. Please make use of them!
@@ -98,9 +98,9 @@Textbook - There is no official textbook for the course. There is a set of course notes available here. These notes - come from an awesome effort of a past CS181 student who decided to create a course textbook as an (unusually + come from an awesome effort of a past CS1810 student who decided to create a course textbook as an (unusually and awesomely ambitious!) senior thesis, and several years of students and staffs who have volunteered time to fix bugs and improve clarity. If you find bugs, please be a good citizen and put in a pull request.
@@ -150,7 +150,7 @@