Lecture Videos

Jan 5 - Jan 7: Neuron Models (week 0)

• Hodgkin-Huxley Neuron Model
• Simpler Neuron Models

Jan 10 - Jan 14: Synapses, Learning (week 0)

• Synapses
• Learning Overview
• Universal Approximation Theorem

Jan 17 - Jan 21: Error Backpropagation (week 1)

• Loss Functions
• Gradient Descent
• Backpropagation

Jan 24 - 28: Automatic Differentiation (week 2)

• Automatic Differentiation: Theory
• Backprop with AD

Jan 31 - Feb 4: PyTorch, Overfitting (week 3)

• PyTorch (notebook)
• Overfitting
• Combatting Overfitting

Feb 7 - Feb 11: Optimization, Autoencoders (week 4)

• Vanishing/Exploding Gradients (in-person Feb 7)
• Enhancing Optimization (in-person Feb 9)
• Autoencoders (no in-person lecture)

Feb 14 - Feb 18: Vision, and CNNs (week 5)

• Your Visual System (in-person Feb 14)
• Convolutional Neural Networks (CNNs) (in-person Feb 18)

Feb 28 - Mar 4: BatchNorm, Hopfield Nets, Variational AE (week 6)

• Batch Normalization (in-person Feb 28)
• Hopfield Networks (video lecture)
• Variational Autoencoders (in-person Mar 4)

Mar 7 - Mar 11: Population Coding (week 7)

• Population Coding (video lecture, Mar 7)
• Transformations (in-person, Mar 9)
• Dynamics (in-person, Mar 11)

Mar 14 - Mar18: Recurrent Neural Networks (week 8)

• RNNs and BPTT (in-person)
• Long Short-Term Memory (LSTM) (in-person)
• Brain-Machine Interfaces (brief), U-Session exercises (in-person)

Mar 21 - Mar 25: Adversarial Attacks (week 9)

• Adversarial Attacks (in-person)
• Adversarial Defense (in-person)
• GANs (in-person)

Mar 28 - Apr 1: Advanced Topics (week 10)

• Vector Embeddings (in-person)
• Predictive Coding (in-person)