伯克利最新无监督深度学习课程资源放出！

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伯克利最新的无监督深度学习CS294-158课程资源放出啦！建议先收藏慢慢看！

这个课程涵盖了两个不需要标记数据的深度学习领域：深度生成模型（ Deep Generative Models）和自主学习（Self-supervised Learning）。近年来生成模型的发展使得对高维原始数据建模成为可能，如自然图像、音频和文本语料库等。

自监督学习的进步已经开始缩小监督表示学习和非监督表示学习之间的差距，对看不见的任务进行微调。

课程涵盖了这两类领域的理论基础和最新的应用，一共有14周，每周的课程都有相应的PDF课件和YouTube讲课视频。

课程目录如下：

Week 1 (1/30)

Lecture 1a: Logistics

Lecture 1b: Motivation

Lecture 1c: Likelihood-based Models Part I: Autoregressive Models

Week 2 (2/6)

Lecture 2a: Likelihood-based Models Part I: Autoregressive Models (ctd) (same slides as week 1)

Lecture 2b: Lossless Compression

Lecture 2c: Likelihood-based Models Part II: Flow Models

Week 3 (2/13)

Lecture 3a: Likelihood-based Models Part II: Flow Models (ctd) (same slides as week 2)

Lecture 3b: Latent Variable Models - part 1

Week 4 (2/20)

Lecture 4a: Latent Variable Models - part 2

Lecture 4b: Bits-Back Coding

Week 5 (2/27)

Lecture 5a: Latent Variable Models - wrap-up (same slides as Latent Variable Models - part 2)

Lecture 5b: ANS coding (same slides as bits-back coding)

Lecture 5c: Implicit Models / Generative Adversarial Networks

Week X (3/6)

Final Project Discussion

Week 6 (3/13)

Lecture 6a: Implicit Models / Generative Adversarial Networks (ctd) (same slides as 5c)

Lecture 6b: Non-Generative Representation Learning [UPDATED 3/24]

Week 7 (3/20)

Lecture 7: Non-Generative Representation Learning (same slides as 6b)

Week 8 (4/3)

Lecture 8a: Strengths/Weaknesses of Unsupervised Learning Methods Covered Thus Far

Lecture 8b: Semi-Supervised Learning

Lecture 8c: Guest Lecture by Ilya Sutskever

Week 9 (4/10)

Lecture 9a: Unsupervised Distribution Alignment

Lecture 9b: Guest Lecture by Alyosha Efros

Week 10 (4/17)

Lecture 10: Language Models (Alec Radford)

Week 11 (4/24)

Lecture 11: Representation Learning in Reinforcement Learning

Week 12 (5/1)

Lecture 12: Guest Lecture by Aaron van den Oord [slides not available]

Week 13 (5/8)

RRR week: no lecture

Week 14 (5/15)

Final Project Presentations

讲解这门课程的一共有四位：

第一位是Pieter Abbeel，他是伯克利机器人学习实验室主任，伯克利人工智能研究实验室(BAIR)联合主任，还是OpenAI的科学家兼顾问；

第二位Peter Chen是Pieter Abbeel教授组里的博士研究生，也是OpenAI的研究员；

其余两位Jonathan Ho和Aravind Srinivas也都是Pieter Abbeel教授组里的博士研究生。

课程链接：

https://sites.google.com/view/berkeley-cs294-158-sp19/home

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