601.765 Linguistic and Sequence Modeling - Questionnaire (spring 2019)

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Name *

Nickname

What do you look like? (Please give enough detail so that we can tell you apart from the other students. Do you have a lightning-shaped scar? Blue glasses? A jacket you always wear?)

Department, degree, year *

Status in this class *

Enrolled for a grade

Hoping to get enrolled for a grade

Auditing, P/F, S/U, or attending informally

Other:

Which of these other relevant courses are you taking this semester? *

601.382 Deep Learning Lab (recommended)

601.431/631 Theory of Computation

601.448/648 Computational Genomics: Data Analysis

601.464/664 Artificial Intelligence

601.475/675 Machine Learning

601.476/676 Machine Learning: Data to Models

601.482/682 Machine Learning: Deep Learning

601.783 Vision as Bayesian Inference

None of the above

Other:

Required

Main research or application area

Tell us about the kind of problems you're currently working on or hope to work on in future.

Most relevant past work

What's the most complicated work you've done that seems related to this class? (A research project, or maybe just the NLP class or another class.)

What do you want to get out of this class?

Which of these stats/ML topics are you reasonably familiar with?

Bayesian networks (directed graphical models)

Markov random fields (undirected graphical models)

Gibbs sampling or MCMC

Importance or rejection sampling

Particle filtering

A* search

Game trees or alpha-beta pruning

Applications of linear algebra to ML (e.g., SVD/PCA for dimensionality reduction, or CCA)

Kalman filters

Gaussian processes

Bandit algorithms

Reinforcement learning

Machine learning course (such as 601.475/675)

Full probability course (such as 553.420/620)

Full statistics course (such as 553.430/630)

AI class (such as 601.464/664, formerly 600.435)

Other:

How familiar are you with Python?

Know the core language

Know how to get things done via common idioms and libraries

Familiar with Jupyter (IPython) notebooks

Familiar with numpy

Familiar with other libraries for statistical or scientific computing

Familiar with matplotlib or other visualization libraries

Which of these neural net topics or skills do you already have?

Could explain at least a basic neural net architecture

Could explain back-propagation

Could explain where word embeddings come from

Could explain recurrent neural nets

Could explain convolutional neural nets

PyTorch

DyNet

TensorFlow

Theano

Caffe

Other:

Option 1

Clear selection

Other comments or concerns about yourself? (If you have more general questions or suggestions for us, please post on Piazza instead of here.)

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