Few small tweaks.

Author: peterschindler

content/info.qmd

@@ -35,35 +35,35 @@ title: Course Information
 There are **no formal prerequisites for this course**. However, an undergraduate level understanding of materials science and basic coding 
 skills in Python can be beneficial. Python will be introduced during the first two weeks through an intensive crash course. In addition, 
 core atomistic and materials science concepts will be (re)introduced by week 4.<br>
-**No prior experience in machine learning is required.**
+**No prior experience in machine learning (ML) is required.**
 
 
 ## Course Format
 
 The course will begin with lectures and **interactive Python demonstrations** during the first two-thirds of the semester, followed by 
 student-led presentations of recent papers, **guest lectures by experts**, and final project presentations in the final third of the 
 semester. Code, slides, and homework assignments will be shared on GitHub. One group project, one individual project, and a recent 
-paper presentation will ensure that students learn state-of-the-art materials informatics skills and can conduct research in this 
-new domain independently. The results obtained in the group project are targeted to be **published in a peer-reviewed journal**.
+paper presentation will ensure that students learn state-of-the-art materials informatics skills and can conduct research independently in this 
+new domain. The results obtained in the group project are targeted to be **published in a peer-reviewed journal**.
 
 ## Who is this for?
 
 This course may be of interest to students of **various backgrounds**, for example, for students with a background in
 
-- engineering/materials/chemistry/physics who have little coding experience but want to learn about how they can use machine learning in  materials science \& chemistry.
-- computer science (i.e., experienced with coding) but little to no background in materials who want to learn about how machine learning can be applied in the materials science \& chemistry domain.
+- engineering/materials/chemistry/physics who have little coding experience but want to learn about how they can use ML in  materials science \& chemistry.
+- computer science (i.e., experienced with coding) but little to no background in materials who want to learn about how machine ML can be applied in materials science \& chemistry.
 - experimental research in the lab with little coding experience, who want to learn how they can use Python to complement their experimental work.
 
 ## Resources
 
 Materials Informatics is a new and rapidly evolving field and hence, conventional textbooks are often not 
 an ideal place to start. This course will not follow a specific textbook but will rely on prior efforts of 
-various aspects of this field (ML, Python, data science, materials science, etc.):
+various aspects of this field (ML, Python, data science, materials science, etc.) and free online resources:
 
 - *Machine Learning in Materials Science*, Keith T. Butler, Felipe Oviedo and Pieremanuele Canepa, ACS (2022). [Link (free with NEU login)](https://doi.org/10.1021/acsinfocus.7e5033)
 - *Machine Learning Refined*, Jeremy Watt, Reza Borhani, and Aggelos K. Katsaggelos, 2nd Edition (2024). [Link (free)](https://github.com/neonwatty/machine-learning-refined)
 - *Understanding Deep Learning*, Simon J.D. Prince, The MIT Press (2023). [Link (free)](https://udlbook.github.io/udlbook/)
-- *Deep Learning for Molecules and Materials*, White, Andrew D, Living Journal of Computational Molecular Science (2021). [Link (free)](https://dmol.pub)
+- *Deep Learning for Molecules and Materials*, Andrew D. White, Living Journal of Computational Molecular Science (2021). [Link (free)](https://dmol.pub)
 
 Further detailed resources and readings on Python and ML will be shared alongside the course material.
 

index.qmd

@@ -6,7 +6,7 @@ page-layout: full
 
 ## Course Summary
 
-Machine Learning (ML) for Materials Science and Discovery is a 4 semester-hour special topics graduate course. 
+Machine Learning for Materials Science and Discovery is a 4 semester-hour special topics graduate course. 
 It covers the basics of machine learning and data science as it applies to materials science problems. Large 
 materials databases, like the Materials Project, are utilized interactively. Common data types originating 
 from computational and experimental methods are surveyed. The materials informatics paradigm is introduced