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MLPR lecture log, Autumn 2017
Here is where I will put any scans or recordings from each lecture. If
anything is unclear, please refer to the actual class
notes. Also, please still take your own notes in class!
Videos take at least an hour to process. They are available in a fancy web
interface on “Media Hopper Replay” / “Echo 360” To get there,
go to Learn, once you are signed up for the
class, and follow the "Media Hopper Replay" link in the sidebar for the class.
I won't use Learn for anything else to do with the class.
There were .mp4 file links here but the videos are no longer available. Sorry.
PDF scans of what I wrote under the document camera in class, and mp4 videos:
- Lecture 1, Monday week 1:
pdf
Logistics and motivation.
w0a–w0f, w1a
- Lecture 2, Wednesday week 1:
pdf
Linear functions and least squares.
w1b
- Lecture 3, Thursday week 1:
pdf
Introduction to basis functions and L2 regularization.
w1c
- Lecture 4, Monday week 2:
pdf
Generalization and dataset splits.
w2a
- Lecture 5, Wednesday week 2:
pdf
More generalization, Gaussians, CLT, standard error bars.
w2b, w2c, w2d
- Lecture 6, Thursday week 2:
pdf
Different sources of variability. Multivariate Gaussians.
w2d, w2e
- Lecture 7, Monday week 3:
pdf
Gaussian classifiers. Regressing on labels.
w3a
- Lecture 8, Wednesday week 3:
pdf
More on baseline classifiers, some calculus.
w3b
- Lecture 9, Thursday week 3:
pdf
Gradients, linear regression to logistic regression.
w3c
- Lecture 10, Monday week 4:
pdf
Stochastic gradients, softmax regression
w3b,
w4a
- Lecture 11, Wednesday week 4:
pdf
Robust logistic regression (cost functions from probabilistic models)
w4a
- Lecture 12, Thursday week 4:
pdf
Feedforward neural nets
w4b
- Lecture 13, Monday week 5:
pdf
Fitting neural nets, start of back-propagation
w4c,
w5a
- Lecture 14, Wednesday week 5:
John Quinn guest lecture: Jupyter notebook.
- Lecture 15, Thursday week 5:
pdf
Reverse mode differentiation with matrices (+ reflection on Quinn's lecture and mid-semester survey)
w5a
- Lecture 16, Monday week 6:
pdf
Autoencoders and PCA
w6a
- Lecture 17, Wednesday week 6:
pdf
PCA continued, SVD, Netflix prize, privacy
w6a,
w6b
- Lecture 18, Thursday week 6:
pdf, extra slides
mp4
Probabilistic and Bayesian regression
The video cut off a few seconds from the end, but nothing important. I just said the posterior is some Gaussian, as at the end of the typeset notes:
w6c
- Lecture 19, Monday week 7:
pdf
Bayesian inference and prediction
w7a
- Lecture 20, Wednesday week 7:
pdf
Bayesian linear regression review, and Bayesian model choice
w7b
- Lecture 21, Thursday week 7:
pdf
More on probabilistic reasoning, and start of Gaussian processes
Matlab/Octave or
Python demo to match start of lecture,
w7c
- Lecture 22, Monday week 8:
pdf
More on Gaussian processes
w7c,
w8a
A minimal GP demo: matlab/octave, python
Alternative GP demo: matlab/octave, python
- Lecture 23, Wednesday week 8:
pdf
Finish GPs (with kernel logistic regression aside), start Bayesian logistic regression
GP readings as in Lecture 22
Preview of w8b
- Lecture 24, Thursday week 8:
pdf
Bayesian logistic regression and the Laplace approximation
w8b
- Lecture 25, Monday week 9:
pdf
More on Gaussian approximations, KL-divergence and variational methods
w8c,
w9a
- Lecture 26, Wednesday week 9:
pdf
Variational methods continued
w9a,
w9b
A minimal stochastic variational inference demo: Matlab/Octave: single-file, more complete tar-ball; Python version.
- Lecture 27, Thursday week 9:
pdf
Mixtures of Gaussians for clustering and density estimation
w9c
- Lecture 28, Monday week 10:
pdf
Bound-based optimizers, Newton's method, L1 regularization
w9c,
w10a,
w10b.
- Lecture 29, Wednesday week 10:
pdf
Ensembles, solving different problems
w10c.
- Lecture 30, Thursday week 10:
Exam preparation advice.
That’s all folks! Have a great winter break.