stat212b

Topics Course on Deep Learning for Spring 2016

UC Berkeley, Statistics Department

##Syllabus

1st part: Convolutional Neural Networks

• Invariance, stability.
• Variability models (deformation model, stochastic model).
• Scattering
• Extensions
• Group Formalism
• Supervised Learning: classification.
• Properties of CNN representations: invertibility, stability, invariance.
• covariance/invariance: capsules and related models.
• Connections with other models: dictionary learning, LISTA, Random Forests.
• Other tasks: localization, regression.
• Embeddings (DrLim), inverse problems
• Extensions to non-euclidean domains.
• Dynamical systems: RNNs and optimal control.
• Guest Lecture: Wojciech Zaremba (OpenAI)

2nd part: Deep Unsupervised Learning

• Autoencoders (standard, denoising, contractive, etc.)
• Variational Autoencoders
• Adversarial Generative Networks
• Maximum Entropy Distributions
• Open Problems
• Guest Lecture: Ian Goodfellow (Google)

3rd part: Miscellaneous Topics

• Non-convex optimization theory for deep networks
• Stochastic Optimization
• Attention and Memory Models
• Guest Lecture: Yann Dauphin (Facebook AI Research)

Schedule

Lec1 Jan 19: Intro and Logistics

Lec2 Jan 21: Representations for Recognition : stability, variability. Kernel approaches / Feature extraction. Properties.

Lec3 Jan 26: Convnets / Scattering

Lec4 Jan 28: Scattering Properties

Lec5 Feb 2: Further Scattering

Lec6 Feb 4: Supervised Learning: classfication. Properties of learnt representations

Lec7 Feb 9: Properties of learnt representations. Covariance and Invariance. Estimation properties: above generalization error. Redundancy in parameter space.

Lec8 Feb 11: Connections with other models (DL, Lista, Random Forests, CART)

Lec9 Feb 16: Representations of stationary processes. Properties.

Lec10 Feb 18: Other high level tasks: localization, regression, embedding, inverse problems.

Lec11 Feb 23: Extensions to non-Euclidean domain. Sequential Data RNNs.

Lec12 Feb 25: Guest Lecture ( W. Zaremba, OpenAI )

Lec13 Mar 1: Unsupervised Learning: autoencoders. Density estimation. Parzen estimators. Curse of dimensionality

Lec14 Mar 3: Variational Autoencoders

Lec15 Mar 8: Adversarial Generative Networks

Lec16 Mar 10: Maximum Entropy Distributions

Lec17 Mar 29: Self-supervised models (analogies, video prediction, text, word2vec).

Lec18 Mar 31: Guest Lecture ( I. Goodfellow, Google Brain )

Lec19 Apr 5: Non-convex Optimization: parameter redundancy, spin-glass, optimiality certificates. stability

Lec20 Apr 7: Tensor Decompositions

Lec20 Apr 12: Stochastic Optimization, Batch Normalization, Dropout

Lec21 Apr 14: Reasoning, Attention and Memory: New trends of the field and challenges. limits of sequential representations (need for attention and memory). modern enhancements (NTM, Memnets, Stack/RNNs, etc.)

Lec22 Apr 19: Guest Lecture (Y. Dauphin, Facebook AI Research)

Lec23-25 Oral Presentations