Schedule

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DateDescriptionDeadlines
Week 1
15 Aug
Motivation / Likelihood-based Models Part I: Autoregressive Models
[ « Scribe Notes (.pdf) ] [ « Recording @ YouTube  ]
Week 2
22 Aug
Likelihood-based Models: Autoregressive Models / Flow Models
[ « Scribe Notes (.pdf) ] [ « Recording @ YouTube  ]
Week 3
29 Aug
Lossless Compression / Flow Models
[ « Scribe Notes (.pdf) ] [ « Recording @ YouTube  ]
Week 4
5 Sep
Lecture 3a: Likelihood-based Models Part II: Flow Models (ctd) (same slides as week 2) / Lecture 3b: Latent Variable Models - part 1
[ « Scribe Notes (.pdf) ] [ « Recording @ YouTube  ]
Week 5
12 Sep
Lecture 4a: Latent Variable Models - part 2 / Lecture 4b: Bits-Back Coding
[ « Scribe Notes (.pdf) ] [ « Recording @ YouTube  ]
Week 6
19 Sep
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
[ « Scribe Notes (.pdf) ] [ « Additional Lecture Slides (Ang Shenting) ] [ « Recording @ YouTube  ]
Preliminary project titles and team members due on Slack's #projects
Recess Week
26 Sep
Lecture 6a: Generative Adversarial Networks
[ « Scribe Notes (.pdf) ] [ « Additional Lecture Slides (Takanori Aoki) ] [ « Recording @ YouTube  ]
Week 7
3 Oct
Lecture 6a: Generative Adversarial Networks (ctd)
See consolidated scribe notes from Week Recess ] [ « Additional Lecture Slides (Ang Yi Zhe) ] [ « Recording @ YouTube  ]
Preliminary abstracts due to #projects
Week 8
10 Oct
Lecture 7: Non-Generative Representation Learning (same slides as 6b)
Week 9
17 Oct
Lecture 8a: Strengths/Weaknesses of Unsupervised Learning Methods Covered Thus Far / Lecture 8b: Semi-Supervised Learning / Lecture 8c: Guest Lecture by Ilya Sutskever
Week 10
24 Oct
Lecture 9a: Unsupervised Distribution Alignment / Lecture 9b: Guest Lecture by Alyosha Efros
Week 11
31 Oct
No lecture due to the Singapore Symposium on Natural Language Processing (SSNLP '19).
Week 12
7 Nov
Lecture 10: Language Models (Alec Radford)
Week 13
14 Nov
Lecture 11: Representation Learning in Reinforcement Learning Participation on evening of 15th STePS