Previous post: ML theory with bad drawings Next post: What do neural networks learn and when do they learn it, see also all seminar posts and course webpage. Lecture video (starts in slide 2 since I hit record button 30 seconds too late - sorry!) - slides (pdf) - slides (Powerpoint with ink and animation) … Continue reading A blitz through classical statistical learning theory

Scribe notes by Manos Theodosis Previous post: A blitz through statistical learning theory Next post: Unsupervised learning and generative models. See also all seminar posts and course webpage. Lecture video - Slides (pdf) - Slides (powerpoint with ink and animation) In this lecture, we talk about what neural networks end up learning (in terms of … Continue reading What do deep networks learn and when do they learn it

Scribe notes by Richard Xu Previous post: What do neural networks learn and when do they learn it Next post: TBD. See also all seminar posts and course webpage. lecture slides (pdf) - lecture slides (Powerpoint with animation and annotation) - video In this lecture, we move from the world of supervised learning to unsupervised … Continue reading Unsupervised Learning and generative models

Scribe notes by Praneeth Vepakomma Previous post: Unsupervised learning and generative models Next post: Inference and statistical physics. See also all seminar posts and course webpage. lecture slides (pdf) - lecture slides (Powerpoint with animation and annotation) - video In this blog post, we will focus on the topic of robustness - how well (or … Continue reading Robustness in train and test time

Scribe notes by Franklyn Wang Previous post: Robustness in train and test time Next post: Natural Language Processing (guest lecture by Sasha Rush). See also all seminar posts and course webpage. lecture slides (pdf) - lecture slides (Powerpoint with animation and annotation) - video Digression: Frequentism vs Bayesianism Before getting started, we'll discuss the difference … Continue reading Inference and statistical physics

Scribe notes by Benjamin Basseri and Richard Xu Previous post: Inference and statistical physics Next post: TBD. See also all seminar posts and course webpage. Alexander (Sasha) Rush is a professor at Cornell working in in Deep Learning / NLP. He applies machine learning to problems of text generation, summarizing long documents, and interactions between … Continue reading Natural Language Processing (guest lecture by Sasha Rush)

Scribe notes by Hamza Chaudhry and Zhaolin Ren Previous post: Natural Language Processing - guest lecture by Sasha Rush Next post: TBD. See also all seminar posts and course webpage. See also video of lecture. Lecture slides: Original form: main / bandit analysis. Annotated: main / bandit analysis. Sham Kakade is a professor in the … Continue reading Towards a Theory of Generalization in Reinforcement Learning: guest lecture by Sham Kakade

Scribe notes by Junu Lee, Yash Nair, and Richard Xu. Previous post: Toward a theory of generalization learning Next post: TBD. See also all seminar posts and course webpage. lecture slides (pdf) - lecture slides (Powerpoint with animation and annotation) - video Much of the material on causality is taken from the wonderful book by … Continue reading Causality and Fairness

Blake Bordelon, Haozhe Shan, Abdul Canatar, Boaz Barak, Cengiz Pehlevan [Boaz's note: Blake and Haozhe were students in the ML theory seminar this spring; in that seminar we touched on the replica method in the lecture on inference and statistical physics but here Blake and Haozhe (with a little help from the rest of us) … Continue reading Replica Method for the Machine Learning Theorist: Part 1 of 2