+
+
+
\ No newline at end of file
diff --git a/policies.md b/policies.md
new file mode 100644
index 0000000..7b8bb6e
--- /dev/null
+++ b/policies.md
@@ -0,0 +1,187 @@
+---
+layout: page
+nav_order: 998
+title: Policies
+description: >-
+ Policies
+---
+
+# Policies
+## Coming soon!
+
+
+
+
diff --git a/resources.md b/resources.md
new file mode 100644
index 0000000..93d63be
--- /dev/null
+++ b/resources.md
@@ -0,0 +1,90 @@
+---
+layout: page
+nav_order: 997
+title: Resources
+description: >-
+ Resources
+---
+
+# Resources
+{:.no_toc}
+
+## Table of Contents
+{: .no_toc .text-delta }
+
+1. TOC
+{:toc}
+
+---
+
+## Book
+
+- [Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321)
+
+## Additional Books
+
+* **If you are not familiar with Python programming, use any online tutorial to get a handle of it.** Here is a [Python mini-course](http://ai.berkeley.edu/tutorial.html#PythonBasics).
+* Computer Organization and Design (5th edition), by David Patterson and John Hennessy (aka the "CompOrg Book").
+* Operating Systems: Three Easy Pieces, by Remzi and Andrea Arpaci-Dusseau (aka the "Comet Book").
+* Database Management Systems (3rd edition), by Raghu Ramakrishnan and Johannes Gehrke (aka the "Cow Book").
+* Spark: The Definitive Guide (1st edition), by Bill Chambers and Matei Zaharia (aka the "Spark Book").
+* Data Management in Machine Learning Systems, by Matthias Boehm, Arun Kumar, and Jun Yang.
+* [Deep Learning Book](https://www.deeplearningbook.org/), by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
+* [Designing Machine Learning Systems](https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969), by Chip Huyen.
+
+## Materials
+
+### Student Materials Folder
+We will post relevant materials in [this folder](https://drive.google.com/drive/folders/1h-ludVoe2KlkD-9HfH1go3tVvAxShKdp?usp=sharing) throughout the quarter.
+
+### Related Documentation and Courses
+* [Ray documentation](https://docs.ray.io/en/latest/index.html)
+* [PyTorch documentation](https://pytorch.org/docs/stable/index.html)
+* [Dive into Deep Learning](https://d2l.ai/)
+* [Machine Learning Compilation](https://mlc.ai/)
+
+## Exam Resources
+Coming soon...
+
+
+## Wellness Resources
+
+Your wellbeing matters, and we hope that DSC 204A is _never_ a barrier for your wellbeing. Below are some campus resources that may be helpful. Remember that you are not alone: please reach out to the instructor and TAs whenever needed!
+
+* [University Health Services](https://studenthealth.ucsd.edu/)
+* [UHS Counseling and Psychological Services (CAPS)](https://caps.ucsd.edu/)
+* [Office For Students with Disabilities](https://osd.ucsd.edu/)
+* [Office of Student Advocacy](https://asadvocacy.ucsd.edu/)
+* [Basic Needs Center](https://basicneeds.ucsd.edu/)
+* [Mental Health Resources Guide](https://studenthealth.ucsd.edu/services/mental-health/index.html)
+
+
+
\ No newline at end of file
diff --git a/schedule.md b/schedule.md
new file mode 100644
index 0000000..f0507b2
--- /dev/null
+++ b/schedule.md
@@ -0,0 +1,40 @@
+---
+layout: page
+nav_order: 995
+title: Schedule Overview
+description: >-
+ Schedule Overview
+---
+
+# Lecture Schedule
+{:.no_toc}
+
+- This schedule might change slightly during the quarter. The dates of the exam, however, will not change.
+- Slides will be uploaded to the course [home page](index.md), typically before each lecture. The lectures themselves might deviate significantly from the textbooks. Thus, it is necessary to attend a lecture live or view its video asynchronously to keep up with course content.
+- The industry guest lectures are not included in the syllabus for the exams. But they will be the focus of the extra credit activities.
+- Some topics may take a few weeks to cover.
+
+|Week|Topic|References|
+|--|--|--|
+|1-2|Basics of Machine Resources: Computer Organization|Ch. 1, 2.1-2.3, 2.12, 4.1, and 5.1-5.5 of CompOrg Book|
+|2-3|Basics of Machine Resources: Operating Systems |Ch. 2, 4.1-4.2, 6, 7, 13, 14.1, 18.1, 21, 22, 26, 36, 37, 39, and 40.1-40.2 of Comet Book|
+|4|Basics of Cloud Computing| - |
+|4-5| Parallel and Scalable Data Processing: Parallelism Basics |Ch. 9.4, 12.2, 14.1.1, 14.6, 22.1-22.3, 22.4.1, 22.8 of Cow Book; Ch. 5, 6.1, 6.3, 6.4 of MLSys Book|
+|5|Review for midterm in discussion section, 05/09/2023 ||
+|6|Midterm on 5/11/2023 in class||
+|6-7|Parallel and Scalable Data Processing: Scalable Data Access||
+|7|Industry Guest Talk: [Jules Damji](https://www.linkedin.com/in/dmatrix/) from Anyscale Discussing Ray Core on 5/18/2023||
+|7-8|Parallel and Scalable Data Processing: Data Parallelism ||
+|8|Industry Guest Talk: [Venkatesh Ravichandran](https://www.linkedin.com/in/venkateshcr/) from Alexa AI at Amazon Discussing Language Data Organization on 5/25/2023||
+|9-10|Dataflow Systems|Ch. 2.2 of MLSys Book|
+|10|ML Model Building Systems|Ch. 8-8.4 of MLSys Book|
+|10|Final exam review in discussion section, 06/06/2023||
+|11|Final Exam on 6/15/2023| - |
+
+
+
+
diff --git a/staff.md b/staff.md
new file mode 100644
index 0000000..72b089c
--- /dev/null
+++ b/staff.md
@@ -0,0 +1,58 @@
+---
+layout: page
+title: Staff
+description: DSC 204A Winter 2024 Staff
+---
+
+# Staff
+
+## Instructor
+
+{% assign instructors = site.staffers | where: 'role', 'Instructor' %}
+
+
+{% for staffer in instructors %}
+{{ staffer }}
+{% endfor %}
+
+{% for staffer in teaching_assistants %}
+{{ staffer }}
+{% endfor %}
+
+
+
+
+
+
diff --git a/syllabus.md b/syllabus.md
new file mode 100644
index 0000000..cac28ba
--- /dev/null
+++ b/syllabus.md
@@ -0,0 +1,186 @@
+---
+layout: page
+nav_order: 2
+title: Syllabus
+description: >-
+ Syllabus
+---
+
+# Syllabus
+
+{:.no_toc}
+
+## Table of Contents
+{: .no_toc .text-delta }
+
+1. TOC
+{:toc}
+
+---
+
+The course is organized into four parts, covering the following topics.
+
+1. **Foundations of Data Systems**: Data models, big data storage and retrieval, and how to encode information when you store data.
+2. **Scaling Distributed Systems**: Cluster, cloud, edge, network, replication, partition, consistency, ACID.
+3. **Data Processing and Programming model**: Batch processing, stream processing, MapReduce, Hadoop, Spark, Ray.
+4. **Machine Learning Systems**: GPUs, TensorFlow, PyTorch, Large language model training and serving systems.
+
+
+A major component of this course is hands-on Python programming to implement data exploration, data preparation, distributed deep learning training, and model selection pipelines on large real-world data using popular libraries (Ray, PyTorch) and cloud resources (e.g., AWS/GCP).
+
+## Logistics
+
+**Lectures**: Monday, Wednesday, Friday 10:00 AM - 10:50 AM.
+
+**Location**: [PCYNH 122](https://maps.app.goo.gl/3xRUTjk3cyVA7wAr6).
+
+**Instructor**: [Hao Zhang](https://cseweb.ucsd.edu/~haozhang/); Office: [HDSI 440](https://maps.app.goo.gl/XeWAcFsFk5bcs2ar5).
+
+## Course Content and Format
+
+- The class meets 3 times a week for 50-minute lectures in person.
+
+ - Attending the lectures is not mandatory. But there are Peer Instruction activities involving discussing questions with peers in class only (details below). There will be other interactive activities as well.
+ - We will use [Piazza](https://piazza.com/ucsd/winter2024/dsc204a) for asynchronous discussions and questions.
+
+- 3 Programming Assignments (PAs).
+ - See the PAs page for the PA schedule and details.
+ - There are no late days for the PAs. Plan your work accordingly.
+
+
+
+- Exams.
+ - **There is NO midterm**. Cheers!
+ - There will be a final exam,
+ - The final exam will be held as a Canvas Quiz only. The dates and logistics are listed below.
+ - The exams will have primarily multiple choice questions (MCQ). Quantitative/longer problems wil exist but only the final answer may need to be selected. Some questions will have partial credits.
+ - The guideline for time per question is a max of 1min per point. The points of each question will be calibrated accordingly.
+ - If you miss an exam, you will get no credit for it, unless you notify the instructor in advance with a university approved reason and receive a makeup exam slot.
+ - The midterm exam is closed books/notes/electronics/Web. You are allowed to keep with you two A4-sized sheets (four sides) with any content you want.
+ - The final exam is open books/Web/etc. The only requirement is you should neither give nor receive help from anyone by any means.
+
+- There will be 3 extra credit Peer Evaluation activities delivered via Canvas only. I will announce more details on these in due course.
+- I will release ungraded exercises on the exercises page throughout the quarter. These questions will act as practice for the graded exams and surprise quizzes.
+- The discussion slots will be used by the TAs to give talks about the PAs. I might also use them for review discussions before the two exams.
+
+## Pre-requisites
+
+DSC 202 (Data Mgt for Data Science); or substantial practical experience with scalable data systems and ML algorithms, subject to the consent of the instructor.
+
+Proficiency in Python programming.
+
+
+## Grading
+
+**Components**:
+- Programming Assignments: 8% + 16% + 16%
+- Final Exam: 40%
+- Reading summary: NeruIPS format, once per week
+- In-class Peer Instruction Activities: 20%
+-
+- Extra Credit: 4% (likely)
+
+
+**Cutoffs**:
+The grading scheme is a hybrid of absolute and relative grading. The absolute cutoffs are based on your absolute total score. The relative bins are based on your position in the total score distribution of the class. The better grade among the two (absolute-based and relative-based) will be your final grade.
+
+
+
+
+
+
Grade
+
Absolute Cutoff (>=)
+
Relative Bin (Use strictest)
+
+
+
A+
+
95
+
Highest 5%
+
+
+
A
+
90
+
Next 10% (5-15)
+
+
+
A-
+
85
+
Next 15% (15-30)
+
+
+
B+
+
80
+
Next 15% (30-45)
+
+
+
B
+
75
+
Next 15% (45-60)
+
+
+
B-
+
70
+
Next 15% (60-75)
+
+
+
C+
+
65
+
Next 5% (75-80)
+
+
+
C
+
60
+
Next 5% (80-85)
+
+
+
C-
+
55
+
Next 5% (85-90)
+
+
+
D
+
50
+
Next 5% (90-95)
+
+
+
F
+
< 50
+
Lowest 5%
+
+
+
+**Example**: Suppose the total score is 82 and the percentile is 33. So, the relative grade is B-, while the absolute grade is B+. The final grade then is B+.
+
+**Non-Letter Grade Options**: You have the option of taking this course for a non-letter grade. The policy for P in a P/F option is a letter grade of C- or better; for S in an S/U option is a letter grade of B- or better.
+
+
+## Exam Dates
+
+- Final Exam: TBD (in the week of March 16 - 23); time window: 3:00-7:00pm PT; time limit: 4 hours
+
+## Classroom Rules
+- No late days for submitting the PAs. No extensions on the final exam time window. Plan all your work well up front accordingly.
+- Students are encouraged to ask questions and participate in discussions in class and on Ed. Please raise your hand before speaking and the instructor will call on you to speak.
+- Please review UCSD's honor code and policies and procedures on [academic integrity](https://academicintegrity.ucsd.edu/) here. If plagiarism is detected in your code, or if we detect collusion on the graded quizzes or exams, or if you are found to be using someone else's clickers, or if any other form of academic integrity violation is identified, you will get zero for that component of your score and get downgraded substantially. I will also notify the University authorities for appropriate disciplinary action to be taken, up to and including expulsion from the University.
+- Please review UCSD's principles of community and our commitment to creating an inclusive learning environment on [this website](https://ucsd.edu/about/principles.html).
+- Harassment, discrimination, or intimidation of any form against any student will not be tolerated in class or on Piazza. Please review UCSD's policies on dealing with harassment and discrimination on [this website](https://ophd.ucsd.edu/).
+
+
+
+
+
+