**First Day of Classes (Tuesday, January 18)**

**Lecture 1 (Tuesday, January 18): Introduction**

**Lecture 2 (Thursday, January 20): Forward and Backpropagation**

**Lecture 3 (Tuesday, January 25): Optimization**

**Lecture 4 (Thursday, January 27): Regularization**

**Lecture 5 (Tuesday, February 1): Convolutional neural networks (CNNs)**

**Lecture 6 (Thursday, February 3): Sequence models (RNNs, LSTM, GRU)**

**Lecture 7 (Tuesday, February 8): Graph neural networks (GNNs)**

**Lecture 8 (Thursday, February 10): Transformers**

Introduction, general purpose Transformer architectures, BERT

Self-attention. multi-head attention, Transformer, positional encoding, encoder, decoder, pre-training and fine-tuning

Transformer models: (i) auto-encoding Transformers, (ii) auto-regressive Transformers, (iii) sequence to sequence Transformers, GPT-3

Vision Transformers, multi-modal Transformers, text and code Transformers, OpenAI Codex.

**Lecture 9 (Tuesday, February 15): Generative adversarial networks (GANs)**

**Lecture 10 (Thursday, February 17): Variational autoencoders (VAEs)**

**Lecture 11 (Tuesday, February 22): Normalizing flows**

**Lecture 12 (Thursday, February 24): Reinforcement learning**

**Lecture 13 (Tuesday, March 1): Reinforcement learning**

**Lecture 14 (Thursday, March 3): Deep reinforcement learning**

**Lecture 15 (Tuesday, March 8): Deep reinforcement learning**

**Lecture 16 (Thursday, March 10): Deep reinforcement learning**

**Spring recess, academic holiday, no classes (Monday, March 14 - Friday, March 18)**

**Lecture 17 (Tuesday, March 22): Competition presentations**

**Lecture 18 (Thursday, March 24): Automated machine learning**

**Lecture 19 (Tuesday, March 28): Multi-task learning**

**Lecture 20 (Thursday, March 31): Meta and transfer learning**

**Lecture 21 (Tuesday, April 5): Online and continual learning**

**Lecture 22 (Thursday, April 7): Deep learning for proteomics**

**Lecture 23 (Tuesday, April 12): Deep learning for robotics**

**Lecture 24 (Thursday, April 14): Deep learning for space**

**Lecture 25 (Tuesday, April 19): Deep learning for climate science**

**Lecture 26 (Thursday, April 21): Deep learning for quantum computing**

**Lecture 27 (Tuesday, April 26): Deep learning for quantum computing**

**Lecture 28 (Thursday, April 28): Posters session**

**Last Day of Classes (Monday, May 2)**

**Exercise 1: Forward and Backpropagation**

**Exercise 2: Optimization**

**Exercise 3: CNN's**

**Exercise 4: RNN's**

**Exercise 5: GNN's**

**Exercise 6: Transformers**

**Exercise 7: GAN's**

**Exercise 8: VAE's**

**Exercise 9: Meta learning**

**Exercise 10: RL**

**Tutorial 1: PyTorch**

**Tutorial 2: TensorFlow**

**Tutorial 3: Keras**

**Tutorial 4: CNN's with TensorFlow**

**Tutorial 5: RNN's**

**Tutorial 6: dgl.ai, GNN library**

**Tutorial 7: huggingface.co, Transformers library**

**Tutorial 8: pyro.ai, probabilistic programming library**

**Tutorial 9: learn2learn.net, meta learning library**

**Tutorial 10: RLlib, reinforcement learning library**