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schedule [2018/01/29 07:28]
anderson [January]
schedule [2018/02/24 22:21] (current)
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 ===== Announcements ===== ===== Announcements =====
 +
 +**February 24:** Assignment A3 has been updated. ​ It no longer requires the implementation of the Swish activation function.
  
 Lecture videos are available at this [[https://​colostate.instructure.com/​courses/​61937/​external_tools/​2755|CS445 video recordings site]]. Lecture videos are available at this [[https://​colostate.instructure.com/​courses/​61937/​external_tools/​2755|CS445 video recordings site]].
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 | Week 1:\\  Jan 16 - Jan 19    | Overview. Intro to machine learning. Python. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​01 Course Overview.ipynb|01 Course Overview]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​02 Matrices and Plotting.ipynb|02 Matrices and Plotting]] ​ | [[http://​www.labri.fr/​perso/​nrougier/​from-python-to-numpy/​|From Python to Numpy]], Chapters 1 - 2\\ [[http://​www.deeplearningbook.org/​|Deep Learning]], Chapters 1 - 5.1.4  | | Week 1:\\  Jan 16 - Jan 19    | Overview. Intro to machine learning. Python. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​01 Course Overview.ipynb|01 Course Overview]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​02 Matrices and Plotting.ipynb|02 Matrices and Plotting]] ​ | [[http://​www.labri.fr/​perso/​nrougier/​from-python-to-numpy/​|From Python to Numpy]], Chapters 1 - 2\\ [[http://​www.deeplearningbook.org/​|Deep Learning]], Chapters 1 - 5.1.4  |
 | Week 2:\\ Jan 22 - Jan 26    | Fitting linear models to data as a direct matrix calculation,​ and incrementally using stochastic gradient descent (SGD)  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​03 Linear Regression.ipynb|03 Linear Regression]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​04 Linear Regression Using Stochastic Gradient Descent (SGD).ipynb|04 Linear Regression Using Stochastic Gradient Descent (SGD)]] ​ | | Week 2:\\ Jan 22 - Jan 26    | Fitting linear models to data as a direct matrix calculation,​ and incrementally using stochastic gradient descent (SGD)  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​03 Linear Regression.ipynb|03 Linear Regression]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​04 Linear Regression Using Stochastic Gradient Descent (SGD).ipynb|04 Linear Regression Using Stochastic Gradient Descent (SGD)]] ​ |
-| Week 3:\\ Jan 29 - Feb 2    | Ridge regression. Data partitioning. ​ Probabilistic Linear Regression. ​  [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​05 Linear Ridge Regression and Data Partitioning.ipynb|05 Linear Ridge Regression and Data Partitioning]] ​ | [[http://​www.deeplearningbook.org/​|Deep Learning]], Section 7.3\\ +| Week 3:\\ Jan 29 - Feb 2    | Ridge regression. Data partitioning. ​ Probabilistic Linear Regression. ​Regression with fixed nonlinearities. ​  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​05 Linear Ridge Regression and Data Partitioning.ipynb|05 Linear Ridge Regression and Data Partitioning]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​06 Probabilistic Linear Regression.ipynb|06 Probabilistic Linear Regression]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​07 Linear Regression with Fixed Nonlinear Features.ipynb|07 Linear Regression with Fixed Nonlinear Features]] ​  |[[http://​www.deeplearningbook.org/​|Deep Learning]], Section 7.3\\  [[http://​www.nytimes.com/​2016/​12/​14/​magazine/​the-great-ai-awakening.html?​_r=0|The Great A.I. Awakening]],​ by Gideon Lewis-Krause,​ NYT, Dec 14, 2016.  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A1 Linear Regression.ipynb|A1 Linear Regression]] due Wednesday, January ​31, 10:00 PM.  Here are some [[http://​www.cs.colostate.edu/​~anderson/​cs445/​goodSolutions|good solutions.]] ​ |
- [[http://​www.nytimes.com/​2016/​12/​14/​magazine/​the-great-ai-awakening.html?​_r=0|The Great A.I. Awakening]],​ by Gideon Lewis-Krause,​ NYT, Dec 14, 2016.  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A1 Linear Regression.ipynb|A1 Linear Regression]] due Tuesday, January ​30, 10:00 PM  |+
  
 ===== February ===== ===== February =====
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 |< 100% 10% 20% 30% 20% 20%  >| |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
-| Week 4:\\ Feb 5 - Feb 9   ​| ​Regression with fixed nonlinearities. Nonlinear ​regression with neural networks. ​ |  +| Week 4:\\ Feb 5 - Feb 9   ​| ​Introduction to nonlinear ​regression with neural networks. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​08 Stochastic Gradient Descent with Parameterized Activation Function.ipynb|08 Stochastic Gradient Descent with Parameterized Activation Function]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​09 Scaled Conjugate Gradient for Training Neural Networks.ipynb|09 Scaled Conjugate Gradient for Training Neural Networks]] ​ | [[http://​www.deeplearningbook.org/​|Deep Learning]], Chapter 6 (skip 6.2)  | 
-| Week 5:\\ Feb 12 - Feb 16  | <color red>​Lectures on Feb 12th and 14th are canceled.</​color> ​ Friday, more neural networks ​ | +| Week 5:\\ Feb 12 - Feb 16  | <color red>​Lectures on Feb 12th and 14th are canceled.</​color> ​ Friday, more neural networks ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​10 More Nonlinear Regression with Neural Networks.ipynb|10 More Nonlinear Regression with Neural Networks]] ​ | 
-| Week 6:\\ Feb 19 - Feb 23  | Autoencoders. ​Recurrent neural networks.  | +| Week 6:\\ Feb 19 - Feb 23  | Autoencoders. ​Activation functions | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​11 Autoencoder Neural Networks.ipynb|11 Autoencoder Neural Networks]] ​ | [[https://​arxiv.org/​pdf/​1710.05941.pdf|Searching for Activation Functions]],​ by Ramachandran,​ Zoph, and Le]]  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A2 Neural Network Regression.ipynb|A2 Neural Network Regression]] due Tuesday, February 20, 10:00 PM  | 
-| Week 7:\\ Feb 26 - Mar 2  | Classification. LDA and QDA. K-Nearest Neighbors. ​ |+| Week 7:\\ Feb 26 - Mar 2  | Classification. LDA and QDA. K-Nearest Neighbors. ​ | |  |[[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A3 Activation Functions.ipynb|A3 Activation Functions]] due Thursday, March 1, 10:00 PM  |
  
 ===== March ===== ===== March =====
schedule.txt · Last modified: 2018/02/24 22:21 (external edit)