Course syllabus and policies: Course handout.
Please do not send the instructor or the TAs email about the class directly to their personal accounts.
Piazza: Students are encouraged to sign up Piazza to join course discussions. If your question is about the course material and doesn’t give away any hints for the homework, please post to Piazza so that the entire class can benefit from the answer.
Lecture and tutorial hours:
|Lecture||Tuesday 3-5 pm||YouTube|
|Tutotiral||Thursday 3-4 pm||Zoom|
Online lectures and tutorials: The access to online lectures and tutorials will be communicated via course mailing list. Course videos and materials belong to your instructor, the University, and/or other sources depending on the specific facts of each situation, and are protected by copyright. Do not download, copy, or share any course or student materials or videos without the explicit permission of the instructor. For questions about recording and use of videos in which you appear please contact your instructor.
It is very hard to hand design programs to solve many real world problems, e.g. distinguishing images of cats v.s. dogs. Machine learning algorithms allow computers to learn from example data, and produce a program that does the job. Neural networks are a class of machine learning algorithm originally inspired by the brain, but which have recently have seen a lot of success at practical applications. They’re at the heart of production systems at companies like Google and Facebook for image processing, speech-to-text, and language understanding. This course gives an overview of both the foundational ideas and the recent advances in neural net algorithms.
|Homework 1||Jan. 17(out), due Jan. 28|
|Programming Assignment 1||TBD||Jan. 21(out), due Feb. 04|
|Homework 2||TBD||Jan. 28(out), due Feb. 11|
|Programming Assignment 2||TBD||Feb. 04(out), due Feb. 25|
|Homework 3||TBD||Feb. 18(out), due Mar. 11|
|Programming Assignment 3||TBD||Mar. 04(out), due Mar. 18|
|Programming Assignment 4||TBD||Mar. 18(out), due Apr. 01|
|Homework 4||TBD||Mar. 25(out), due Apr. 08|
|Course Project||Guideline soon||due Apr. 12|
Midterm Quiz: Feb. 09
The midterm quiz will cover the lecture materials up to lecture 4. The exact details will be announced soon.
Suggested readings included help you understand the course material. They are not required, i.e. you are only responsible for the material covered in lecture. Most of the suggested reading listed are more advanced than the corresponding lecture, and are of interest if you want to know where our knowledge comes from or follow current frontiers of research.
|Lecture 1||Jan 12||Introduction & Linear Models||Slides||Roger Grosse’s notes: Linear Regression, Linear Classifiers, Training a Classifier|
|Tutorial 1||Jan 14||Multivariable Calculus Review||ipynb||Python notebook: ipynb, you may view the notebook via Colab.|
|Lecture 2||Jan 19||Multilayer Perceptrons & Backpropagation||Roger Grosse’s notes: Multilayer Perceptrons, Backpropagation|
|Tutorial 2||Jan 21||Autograd and PyTorch|
|Lecture 3||Jan 26||Distributed Representations & Optimization|
|Tutorial 3||Jan 28||How to Train Neural Networks|
|Lecture 4||Feb 02||Convolutional Neural Networks and Image Classification|
|Tutorial 4||Feb 04||Convolutional Neural Networks|
|Lecture 5||Feb 09||Interpretability|
|Midterm Quiz||Feb 09|
|Tutorial 5||Feb 11||How to Write a Good Course Project Report|
|Reading Week||Feb 16|
|Lecture 6||Feb 23||Optimization & Generalization|
|Tutorial 6||Feb 25||Best Practices of ConvNet Applications|
|Lecture 7||Mar 02||Recurrent Neural Networks and Attention|
|Tutorial 7||Mar 04||Recurrent Neural Networks|
|Lecture 8||Mar 09||Transformers and Autoregressive Models|
|Tutorial 8||Mar 11||NLP and Transformers|
|Lecture 9||Mar 16||Reversible Models & Generative Adversarial Networks|
|Tutorial 9||Mar 18||Information Theory|
|Lecture 10||Mar 23||Generative Models & Reinforcement Learning|
|Tutorial 10||Mar 25||Generative Adversarial Networks|
|Lecture 11||Mar 30||Q-learning & the Game of Go|
|Tutorial 11||Apr 01||Policy Gradient and Reinforcement Learning|
|Lecture 12||Apr 06||TBD|
|Related Textbooks||Deep Learning (Goodfellow at al., 2016)||The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning.|
|Information Theory, Inference, and Learning Algorithms (MacKay, 2003)||A good introduction textbook that combines information theory and machine learning.|
|General Framework||PyTorch||An open source deep learning platform that provides a seamless path from research prototyping to production deployment.|
|Computation Platform||Colab||Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud.|
|GCE||Google Compute Engine delivers virtual machines running in Google’s innovative data centers and worldwide fiber network.|
|AWS-EC2||Amazon Elastic Compute Cloud (EC2) forms a central part of Amazon.com’s cloud-computing platform, Amazon Web Services (AWS), by allowing users to rent virtual computers on which to run their own computer applications.|