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start [2018/01/20 17:07]
127.0.0.1 external edit
start [2018/02/22 23:33] (current)
anderson [February]
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 ====== Schedule ====== ====== Schedule ======
  
-/* 
-Follow this link to view all [[https://​echo.colostate.edu/​ess/​portal/​section/​37e 
-115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]]. 
-*/ 
 ===== Announcements ===== ===== Announcements =====
 +
 +**February 13:** Assignment A2 has been updated and now contains link to A2grader.tar.
  
 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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 |< 100% 10% 20% 30% 20% 20%  >| |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
-| 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://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​03 Linear Regression.ipynb|03 Linear Regression]]  | [[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    |  +| 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    |  |  |  | [[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  |+| 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.]] ​ |
  
 ===== 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   |  +| 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  |  +| 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  |  +| 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  | +| 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 =====
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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 8:\\ Mar 5 - Mar 9   ​| ​+| Week 8:\\ Mar 5 - Mar 9   | Classification with Neural Networks  ​|
 |  Mar 12 - Mar 16  |  Spring Break  | |  Mar 12 - Mar 16  |  Spring Break  |
-| Week 9:\\ Mar 19 - Mar 23 |  +| Week 9:\\ Mar 19 - Mar 23 | Analysis of Trained Networks. Bottleneck Networks. Classifying Hand-Drawn Digits.  ​
-| Week 10:\\ Mar 26 - Mar 30  | +| Week 10:\\ Mar 26 - Mar 30  | Convolutional Neural Networks ​ |
  
 ===== April ===== ===== April =====
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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 11:\\ Apr 2 - Apr 6   |  +| Week 11:\\ Apr 2 - Apr 6   | Reinforcement Learning. Games using Tabular Q functions.  ​
-| Week 12:\\ Apr 9 - Apr 13  |  +| Week 12:\\ Apr 9 - Apr 13  | Reinforcement Learning using Neural Networks as Q functions. ​ | 
-| Week 13:\\ Apr 16 - Apr 20  |  +| Week 13:\\ Apr 16 - Apr 20  ​| Unsupervised Learning. Dimensionality Reduction. ​ Clustering. ​ |
-| Week 14:\\ Apr 23 - Apr 27  | +| Week 14:\\ Apr 23 - Apr 27  | Support Vector Machines. ​ |
  
 ===== May ===== ===== May =====
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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 15:\\ Apr 30 - May 4  | +| Week 15:\\ Apr 30 - May 4  | Ensembles. ​ Other topics. ​ |
 | May 7 - May 10  |  Final Exams  |  | May 7 - May 10  |  Final Exams  | 
  
  
  
start.1516493237.txt.gz · Last modified: 2018/01/20 17:07 by 127.0.0.1