Alex Smola

Lecture 15, Part 3, Axiomatic Explanations

Lecture 15, Part 4, Heuristics

Lecture 15, Part 2, Local Models and Conditioning

Lecture 15, Part 1, Simple Explanations

Lecture 14, Part 3, Fairness in Practice

Lecture 14, Part 2, Fairness and Scores

Lecture 14, Part 1, Fairness Background

Lecture 8, Part 4, Graphs and Networks

Lecture 8, Part 3 More about Sequence Models

Lecture 8, Part 2 Sequence Models

Lecture 8, Part 1 Dependent Random Variables

Lecture 6, Part 2, Covariate Shift

Lecture 6, Part 4, Adversarial Data

Lecture 6, Part 3, More Math for Covariate Shift

Lecture 6, Part 1, Generalization

Lecture 7, Part 2 Label Shift

Lecture 7, Part 1 - Two-sample tests

5.3 BERT and Applications

5.2 Transformers

5.1 Attention

4.3 Deep and Bidirectional LSTMs

4.2 LSTM and Friends

2.4 Autograd in PyTorch

2.3 Linear Algebra

4.1 Sequence Model Basics

2.2 Data Preprocessing (with Pandas)

3.2 Object Detection Basics

3.1 Preprocessing and Transfer Learning

2.3 Modern Convnets (ResNet, NiN, Inception, ShuffleNet, etc.)

2.2 LeNet and AlexNet