Deep Learning

DATASCI 315, Fall 2025

This is an introductory deep learning course using the Python programming language and the PyTorch deep learning library.

• Textbook: Understanding Deep Learning by Simon J.D. Prince. Print version is available from MIT Press and an online version is here.

• Undergraduate Courses on Deep Learning: Many universities now offer an introductory deep learning course, e.g., Berkeley, CMU, MIT, Stanford
• Canvas: You should access the Canvas class page (link TBD) for this course frequently. It will let you access important announcements and track course deliverables. (requires UM login)
• Days and Times: Tuesdays and Thursdays, 11:30-1
• Location: 2260 USB (links for virtual lectures, if any, will be saved in the syllabus page on canvas)

Instructor Information

Name: Ambuj Tewari
Office Hours: TBD
Email: tewaria@umich.edu

GSI Information

Name: TODO
Email: someone@umich.edu

Lab webpage (also has GSI office hours info): TBD

Grading

• Canvas quizzes (20%): Timed, multiple choice. Will drop two lowest scores. Books, notes, internet, generative AI use permitted but discouraged.
• Homeworks (30%): One programming heavy homework per course module. Will drop the lowest score. Generative AI use allowed.
• Midterm Exam (20%): In class, timed, multiple choice, closed book, no devices. Generative AI use prohibited.
• Final Exam (30%): In class, timed, multiple choice, no devices. Books, notes, internet, generative AI use prohibited.

No credit on late submissions unless there is a documented emergency.

Note on the use of generative AI

As ntoed above, generative AI use is allowed for homeworks. However, it is not allowed in exams. Quizzes are meant as a preparation for exams. It is in your best interest to simulate exams conditions when solving the quizzes.

Academic Integrity

The University of Michigan community functions best when its members treat one another with honesty, fairness, respect, and trust. The college promotes the assumption of personal responsibility and integrity, and prohibits all forms of academic dishonesty and misconduct. All cases of academic misconduct will be referred to the LSA Office of the Assistant Dean for Undergraduate Education. Being found responsible for academic misconduct will usually result in a grade sanction, in addition to any sanction from the college. For more information, including examples of behaviors that are considered academic misconduct and potential sanctions, please see https://lsa.umich.edu/lsa/academics/academic-integrity.html

Accommodation for Students with Disabilities

If you think you need accommodation for a disability, please let me know at your earliest convenience. Some aspects of this course, the assignments, the in-class activities, and the way the course is usually taught may be modified to facilitate your participation and progress. As soon as you make me aware of your needs, we can work with the Office of Services for Students with Disabilities (SSD) to help us determine appropriate academic accommodations. SSD (734-763-3000; http://ssd.umich.edu/) typically recommends accommodations through a Verified Individualized Services and Accommodations (VISA) form. Any information you provide is private and confidential and will be treated as such.

Mental Health and Well-Being

Students may experience stressors that can impact both their academic experience and their personal well-being. These may include academic pressures and challenges associated with relationships, mental health, alcohol or other drugs, identities, finances, etc. If you are experiencing concerns, seeking help is a courageous thing to do for yourself and those who care about you. If the source of your stressors is academic, please contact me so that we can find solutions together. For personal concerns, U-M offers a variety of resources, many which are listed on the Resources for Student Well-being webpage. You can also search for additional well-being resources here.

Syllabus

Preparation for deep learning

• Intro to Python
• Intro to Numpy
• Intro to Pytorch
• 1 Introduction
• 2 Supervised learning
• 3 Shallow neural networks

Deep learning basics

• 4 Deep neural networks
• 5 Loss functions
• 6 Fitting models
• 7 Gradients and initialization
• 8 Measuring performance
• 9 Regularization

Common architectures

• 10 Convolutional networks
• 11 Residual networks
• 12 Transformers
• 13 Graph neural networks

Generative deep learning

• 14 Unsupervised learning
• 15 Generative adversarial networks
• 16 Normalizing flows
• 17 Variational autoencoders
• 18 Diffusion models