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# Differences

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 start [2020/01/21 10:32]127.0.0.1 external edit start [2020/01/23 13:38] (current) 2020/01/22 19:40 anderson [January] 2020/01/21 10:32 external edit Next revision Previous revision 2020/01/22 19:40 anderson [January] 2020/01/21 10:32 external edit Line 6: Line 6: |< 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 21, 23  | Overview of course, kinds of machine learning, python, jupyter notebooks, and mathematics of machine learning.  | [[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/​01a Matrix Multiplication on GPU.ipynb|01a Matrix Multplication on GPU]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​02 Matrices and Plotting.ipynb|02 Matrices and Plotting]] ​ | + | Week 1:\\ Jan 21, 23  | Overview of course, kinds of machine learning, python, jupyter notebooks. ​ | [[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/​01a Matrix Multiplication on GPU.ipynb|01a Matrix Multplication on GPU]]\\ [[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 with SGD.ipynb|03 Linear Regression with SGD]]  | | Week 2:\\ Jan 28, 30  | Supervised learning. Linear and nonlinear regression with artificial neural networks. Gradient derivation and implementation. Adam, SGD. Data partitioning into train, validate and test sets.   | | Week 2:\\ Jan 28, 30  | Supervised learning. Linear and nonlinear regression with artificial neural networks. Gradient derivation and implementation. Adam, SGD. Data partitioning into train, validate and test sets.   |
start.txt ยท Last modified: 2020/01/23 13:38 (external edit)