Deep Learning for Perception

Virginia Tech, Electrical and Computer Engineering

Fall 2015: ECE 6504

• Course Information
• Schedule
• Grading
• Late Policy
• Prerequisites
• FAQs
• Reviews
• Presentations
• Projects
• Related Classes

Course Information

This is an exciting time to be studying (Deep) Machine Learning, or Representation Learning, or for lack of a better term, simply Deep Learning!

Deep Learning is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of applications (vision, language, speech, computational biology, robotics, etc), leading to some pretty significant commercial success.

This course will expose students to cutting-edge research — starting from a refresher in basics of neural networks, to recent developments. The emphasis will be on student-led paper presentations and discussions. Each “module” will begin with instructor lectures to present context and background material.

NOTE: Only the lectures delivered by the course instructor are made public. We regret that student presentations cannot be made public due to privacy reasons.

Instructor

Dhruv Batra

Office Hour

Fri 3-4pm, Whittemore 468

Teaching Assistants

Abhishek Das
Ashwin Kalyan

Office Hours

Ashwin Kalyan: Mon 3-4pm, Whittemore 415
Abhishek Das: Fri 3-4pm, Whittemore 415

Class meets

Tue, Thu 5:00 - 6:15, SURGE 107

Scholar Site

https://scholar.vt.edu/portal/site/f15ece6504

Q&A and Discussions

Scholar Discussion Forum

Staff Mailing List

staff-f15ece6504-g@vt.edu

Schedule

Grading

• 40% Homework (4 homeworks x 10% each)
• 15% Paper Presentation
• 15% Paper Reviews
• 25% Class Project
• 5% Class Participation

Late policy for deliverables

• No penalties for medical reasons (bring doctor's note) or emergencies.
• Every student has 5 free late days (5 x 24-hour chunks) for this course; these cannot be used for HW0, the reviews or the presentation.
• After all free late days are used up, penalty is 25% for each additional late day.

Prerequisites

ECE 6504 is an ADVANCED class. This should not be your first exposure to machine learning. Ideally, you need:

• Intro-level Machine Learning
  • ECE 5984 or equivalent
• Algorithms
  • Dynamic programming, basic data structures, complexity (NP-hardness)
• Calculus and Linear Algebra
  • positive semi-definiteness, multivariate derivates (be prepared for lots and lots of gradients!)
• Programming
  • This is a demanding class in terms of programming skills.
  • HWs will involve a mix of languages (Python, C++, and Lua) and libraries (Caffe and Torch).
  • Your language of choice for project.
• Ability to deal with abstract mathematical concepts

FAQs

• The class is full. Can I still get in?

  See the advisors in 340 Whittemore Hall about force-add forms, and come to the first class. There will be a HW0 released on day 1 to check background preparation. Our past experience with HW0 suggests that slots will open up after some students drop the class.

• I have not taken an “Intro to Machine Learning” class or I am taking it in parallel. Can I still take this class?

  Try working on HW0. If terms seem unfamiliar or you find yourself doing significant background reading to even understand the question, you may benefit from waiting for the next offering of this class.

• Can I audit this class?

  Depends. We will give preference to students wanting to take this class for credit, so if the class is full, there will be no audits.

Reviews

• Length <=1 page
• 11 pt Times New Roman, 1 inch margins
• Due: Midnight before class
• Organization of Review:
• Summary:
  • What is this paper about?
  • What is the main contribution?
  • Describe the main approach & results. Just facts, no opinions yet.
• Strengths:
  • Is there a new theoretical insight?
  • Or a significant empirical advance? Did they solve a standing open problem?
  • Or is a good formulation for a new problem?
  • Or a faster/better solution for an existing problem?
  • Any good practical outcome (code, algorithm, etc)?
  • Are the experiments well executed?
  • Useful for the community in general?
• Weaknesses:
  • What would you do differently?
  • Any missing baselines? missing datasets?
  • any odd design choices in the algorithm not explained well? quality of writing?
  • Is there sufficient novelty in what they propose? Has it already been done? Minor variation of previous work?
  • Why should anyone care? Is the problem interesting and significant?
• Reflections:
  • How does this relate to other papers we have read?
  • What are the next research directions in this line of work?

Presentations

• Once in the semester
• Expected to:
  • read all papers listed for that day
  • provide overview of that day’s theme
  • present details of at least 2 papers and explain how different papers relate to each other.
  • please clearly cite the source of each slide that is not your own.
  • not submit paper review for that class
  • meet with TA 3-4 days before class to dry run presentation (Worth 40% of presentation grade)

Projects

• You are encouraged to try out interesting applications of deep learning (vision, NLP,computational biology, UAVs, etc!)
• The project must be done in this semester. No double counting.
• You may combine with other course project but must delineate the different parts
• Extra credit for shooting a publication
• Main Categories:
  • Application/survey - compare a bunch of algorithms on a new application domain of your interest
  • Formulation/Development - Formulate a new model or algorithm for a new/od problem
  • Theory: Theoretically analyze an existing algorithm.

Related Classes / Online Resources

• CS231n Convolutional Neural Networks for Visual Recognition, Stanford
• Machine Learning, Oxford
• Deep Learning, New York University

Book

• Deep Learning, Ian Goodfellow, Aaron Courville, and Yoshua Bengio, Book in preparation for MIT Press

Overviews

• Deep Learning, Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, Nature
• Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, and Pascal Vincent

Note to people outside VT

Feel free to use the slides and materials available online here. If you use our slides, an appropriate attribution is requested. Please email the instructor with any corrections or improvements.

Acknowledgements

We thank NVIDIA for GPU donations and AWS Educate Grant for cloud computing resources.