I. Class info：
Classroom: 17th & Horton A2000 TA Office hour: 17th & Horton DSI space & Zoom Monday 10:30 AM - 12:00 PM , Jialin Yue; Zoom Link Tuesday 3:00 PM - 4:30 PM , Muyang Li; Zoom Link Wednesday 10:30 AM - 12:00 PM , Jialin Yue; Zoom Link Thursday 3:00 PM - 4:30 PM , Muyang Li; Zoom Link Faculty Office hour: Friday 1:00 PM – 2:00 PM, FGH376 or Zoom (*Please reserve before Thursdays) Friday 1:00 PM at Zoom Link
II. Course Information：
Provides students with an understanding of conceptual and practical aspects of models and algorithms used in deep learning. Key topics covered in this course include: Basic mathematical tools and machine learning concepts used in deep learning; Modern practical deep feedforward, convolutional, and recurrent networks; Regularization for deep learning; Optimization for learning deep models; Practical design methods
III. Teaching Team:
Instructor: Yuankai Huo, yuankai.huo@vanderbilt.eduTA: Muyang Li, muyang.li@vanderbilt.edu Jialin Yue, jialin.yue@vanderbilt.edu
IV. Time:
Class Meets: Tuesday & Thursday, 10:00 am – 11:15 am, 17th & Horton A2000
V. Useful links:
Course Website: https://hrlblab.github.io/DS5660.html
Submission & Discussion: https://www.vanderbilt.edu/brightspace
Schedule
|
Date |
Topics |
Comments |
|---|---|---|
| Aug 22 | Overview | all slides |
| Aug 27 | Introduction of deep learning | all homework |
| Aug 29 | From linear regression to deep learning | |
| Sep 03 | From logistic regression to deep learning | |
| Sep 05 | Neural network | |
| Sep 10 | Neural network optimization | HW1 due |
| Sep 12 | workshop 1: environment setting | |
| Sep 17 | workshop 2: PyTorch programming | |
| Sep 19 | Machine learning basics | HW2 due |
| Sep 24 | Deep neural network | |
| Sep 26 | Optimization | project teams, Quiz 1 |
| Oct 01 | Adaptive Learning Rate | HW3 due, mid-term review |
| Oct 03 | Mid-term Exam (in-class) | 90 mins in-person |
| Oct 08 | Final project proposal 1 | |
| Oct 10 | === Fall break === | |
| Oct 15 | Final project proposal 2 | |
| Oct 17 | Workshop3: PyTorch and AutoGrad | Mini project proposal due |
| Oct 22 | Convolution | HW4 due |
| Oct 24 | Convolutional Neural Network | Mid-term Exam (take-home due) |
| Oct 29 | Computer Vision | |
| Oct 31 | Generative Model | Quiz 2 |
| Nov 05 | NLP | HW5 due |
| Nov 07 | Self-attention | |
| Nov 12 | Transformer | |
| Nov 14 | Transformer2 | |
| Nov 19 | Self-supervised Learning and Explainable AI | presentation format, |
| Nov 21 | Generative AI | HW6 due |
| Nov 26 | === Thanksgiving break === | |
| Nov 28 | === Thanksgiving break === | |
| Dec 03 | LSTM | |
| Dec 05 | HuggingFace Workshop | Quiz 3 |
| Dec 10 | (no class) | HW7 due (HW regrade due) |
| Dec 12 | Final presentation poster/report due (no class) | Final project due |
Assignments
|
Assignments |
Download |
Due Date |
|---|---|---|
| Mini Project Proposal | Example | TBD |
| Mini Project Presentation | Instruction | Dec 12 2024, 9AM |
Grading, Homework, Mid Term Exam, and Final Project
More details are provided Here.
Computational Resource
GPU computing is required for this class. All the homework should be done and submitted using Google Colab. You can use Colab or your own/lab’s GPU for the final project since that is the most convenient way of writing and testing code with GUI.
FAQs
1. The class is full. Can I still get in?
It is unlikely except other students drop it during the first week.
2. What is pre-requirement?
Linear algebra, programming in python, introduction in machine learning.
3. Can I sit in class without registering?
Yes after getting the instructor’s approval. Another option is to register to audit the class (just $50).
References
* We used images and contents in the slides from the following resources, thanks for the great work done by the smart people!
https://speech.ee.ntu.edu.tw/~tlkagk/courses.html
https://speech.ee.ntu.edu.tw/~hylee/index.php
http://cs231n.stanford.edu/
http://deeplearning.cs.cmu.edu/
https://www.deeplearningbook.org/lecture_slides.html
https://www.cs.princeton.edu/courses/archive/spring16/cos495/
http://ttic.uchicago.edu/~shubhendu/Pages/CMSC35246.html
https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.php
https://speech.ee.ntu.edu.tw/~hylee/ml/2022-spring.php
https://speech.ee.ntu.edu.tw/~hylee/ml/2023-spring.php
https://www.cc.gatech.edu/classes/AY2018/cs7643_fall
http://introtodeeplearning.com/