updated first chapter

adding link to slides and video
renamed sections of chapter 1

book/Chapter1-GettingStarted/1.1_open_reproducible_science.md

@@ -0,0 +1,4 @@
+# Open Reproducible Science
+
+[Slides](https://docs.google.com/presentation/d/1SCrx65-Q_CB8JRMN81Uk-QniPvPE-wozRD7YDDQEapc/edit?usp=sharing)
+[Video](https://youtu.be/WeZ2vJxBuTg?si=aG1IuBNhCQnb1ljv)

book/_toc.yml

@@ -25,15 +25,17 @@ parts:
     # Unix System
     # - file: # replace
 
+    # Open Science
+    - file: Chapter1-GettingStarted/1.1_open_reproducible_science
     # Python OSE
-    - file: Chapter1-GettingStarted/1.1_python_environment
+    - file: Chapter1-GettingStarted/1.2_python_environment
     # jupyter
-    - file: Chapter1-GettingStarted/1.2_jupyter_environment
+    - file: Chapter1-GettingStarted/1.3_jupyter_environment
     # Computational Environments and Computational Containers
 
-    - file: Chapter1-GettingStarted/1.3_version_control_git
+    - file: Chapter1-GettingStarted/1.4_version_control_git
 
-    - file: Chapter1-GettingStarted/1.4_computational_environments
+    - file: Chapter1-GettingStarted/1.5_computational_environments
 # 2
 # - caption: Python and Python Ecosystem
   # chapters:

book/about_this_book/about_this_book.md

@@ -4,7 +4,7 @@ The **GeoS**cience **MA**chine Learning **R**esources and **T**raining (GeoSMART
 
 This book is used in the course offered at the University of Washington: Machine Learning in the Geoscienes (AUTMN 2022 - ESS 490C/590C). The corresponding GitHub repository with notebooks for the tutorials and homeworks is [MLGeo](https://github.com/UW-ESS-DS/MLGeo-Autumn22). Find the Docker image for the corresponding jupyter hub [MLGeo Image](https://github.com/UW-ESS-DS/MLGeo-image).
 
-Instructor: Marine Denolle ([email protected])
+Instructors: Marine Denolle (mdenolle@uw.edu), Akshay Mehra (akmehra@uw.edu)
 Supported by: the GeoSMART team (Stefan Todoran, Nicoleta Cristea, Anthony Arendt, Scott Henderson, Ziheng Sun)
 
 # Overview
@@ -34,11 +34,10 @@ By the end of the quarter, the students should be able to:
 
 # Syllabus
 
-- Module 1 (weeks 1 and 2): Intro on ML in the Geo and basic tool building for Open Sciences 
-- Module 2: (weeks 3 and 4) Creating Machine-Learning Ready Data Sets
-- Module 3: (weeks 5 and 6) Feature extraction and clustering
-- Module 5: (weeks 7 and 8) Machine Learning
-- Module 6: (weeks 9 and 10) Deep Learning
+- Module 1 (week 1 ): Intro on ML in the Geo and basic tool building for Open Sciences 
+- Module 2: (weeks 2,3,4) Data Wrangling and AI-ready data
+- Module 3: (weeks 5,6,7) Classic ML
+- Module 4: (weeks  8,9,10) Deep Learning
 
 # Technical Skills Building
 Throughout the course, the students will build skills in shell, version control using git and GitHub, python programming, high-performance computing strategies, and simple data visualization using python. 
@@ -53,9 +52,9 @@ Each week, students will write a short report about either a paper or a webinar.
 
 # Github with tutorials and homeworks
 
-The course [GitHub](https://github.com/UW-ESS-DS/MLGeo-Autumn22) has the tutorial notebooks. Clone the tutorial
+The course [GitHub](https://github.com/UW-ESS-DS/MLGeo_2023_) has the tutorial notebooks. Clone the tutorial
 
-        git clone "https://github.com/UW-ESS-DS/MLGeo-Autmn22"
+        git clone "https://github.com/UW-ESS-DS/MLGeo_2023_"
 
 To update the local repository from the remote version
         git fetch