+ src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTWqFsXhSzRUBQ3g42BK553ZfJ7LjJhkc-AwPPFW22mOG67rm1snSvi-WvkM-jwdRa6M-t8cWWOMoJg/pubhtml?widget=true&headers=false">
diff --git a/_data/all.yaml b/_data/all.yaml index d53e3af..108c23c 100644 --- a/_data/all.yaml +++ b/_data/all.yaml @@ -1,16 +1,15 @@ tas: - - name: Charu Badrinath (Head TF) - - name: Alex Cai (Head TF) - - name: Ahmad Abdel-Azim - - name: Audrey Chang - - name: Brian Ham - - name: Alyssa Huang - - name: Sam Jones - - name: Neeyanth Kopparapu - - name: Angela Li - - name: Joshua Park - - name: Gabriel Sun - - name: Jeffrey Xu - - name: Andrew Zhao - - name: Derek Zheng - - name: Shirley Zhu +- name: Sam Jones (Head TF) +- name: Gabriel Sun (Head TF) +- name: Kelsey Chen +- name: Brian Ham +- name: Evan Jiang +- name: Russell Li +- name: Elvin Lo +- name: Annabel Ma +- name: Joshua Park +- name: Johnathan Sun +- name: Lillian Sun +- name: Simon Sun +- name: Eileen Ye +- name: Andrew Zhao \ No newline at end of file diff --git a/_includes/footer.html b/_includes/footer.html index 2b44a72..2fc93dc 100644 --- a/_includes/footer.html +++ b/_includes/footer.html @@ -3,7 +3,7 @@
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 @@
- 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 @@