Deep learning and neural networks are actively transforming data analysis in nearly every branch of physics and astronomy. New techniques for data analysis are leading to new discoveries, more sensitive measurements, and faster and more powerful simulations. This course will survey state of the art techniques in modern machine learning and their applications to physics and astronomy.

ML methods covered in this course will include classification/regression, generative modeling and anomaly detection. Students will be introduced to major ML frameworks and architectures such as CNNs, transformers, GANs, VAEs, normalizing flows and diffusion models.

These methods will be illustrated through their applications to a broad array of subfields, such as particle physics, astronomy, cosmology, condensed matter physics, and more. No prior knowledge of modern ML or domain knowledge of any of these fields will be required. However, there will be an emphasis on hands-on examples, so prior experience in data analysis and coding (Python, numpy, matplotlib, ...) would be helpful to get the most out of the course.

Along the way, students will receive a practical introduction to fundamental concepts and methods in machine learning and data science. Hopefully, after taking this semester-long course, students will be able to understand current research trends in ML applications to physics and astronomy and embark on research projects of their own.

**Instructor:**

Prof. David Shih

Office: Serin E370

Email:
dshih@physics.rutgers.edu

Phone: (848) 445-9072

Office hours: By appointment

**Lectures:** MTh 10:20-11:40, NHETC SEMINAR ROOM

**Resources**:

- The Deep Learning Book. Goodfellow, Bengio and Courville (2016).

**Prerequisites**:

This course is intended for everyone in the department, so NO prior domain knowledge of any particular subfield will be required.
You will also NOT
be required to know in advance any of the deep learning software frameworks (Keras, Tensorflow, pytorch, etc).
However, you will be expected to be at least somewhat familiar with python and some of its essential packages (numpy, matplotlib, ...).

**Homeworks:**
TBD

**Exams:**
TBD

**Students with disabilities:**
Please read here.