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--- start [2017/08/24 17:03]
anderson
+++ start [2017/08/30 09:26]
127.0.0.1 external edit
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 ===== Announcements =====
+Aug 30: The A1grader.tar file in Assignment 1 has been updated
 Lecture videos are available from the Canvas site (in the menu on the left) by selecting [[https://colostate.instructure.com/courses/55296/external_tools/2755|Echo 360]].
+To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:
+  export PATH=/usr/local/anaconda/bin:$PATH
 /*
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 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
 | Week 1:\\  Aug 21 - Aug 25    | What is AI?  Promises and fears.\\ Python review.\\ Problem-Solving Agents.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/01 Introduction to AI.ipynb|01 Introduction to AI]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/02 Introduction to Python.ipynb|02 Introduction to Python]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/03 Problem-Solving Agents.ipynb|03 Problem-Solving Agents]]   | Chapters 1, 2, 3.1.\\ [[http://science.sciencemag.org/content/357/6346/7.full|AI, People, and Society]], by Eric Horvitz.\\ [[https://aeon.co/essays/can-we-design-machines-to-make-ethical-decisions|Automated Ethics]], by Tom Chatfield.\\ [[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.\\ [[https://www.commondreams.org/news/2017/07/19/fundamental-existential-threat-lawmakers-warned-risks-killer-robots|"Fundamental Existential Threat": Lawmakers Warned of the Risks of Killer Robots]], by Julia Conley\\ Section 1 of [[http://www.scipy-lectures.org|Scipy Lecture Notes]]   |  |
-| Week 2:\\ Aug 28 - Sept 1    |  Problem-solving search and how to measure performance.\\ Iterative deepening and other uninformed search methods.   |     | Sections 3.1 - 3.4  |   |
+| Week 2:\\ Aug 28 - Sept 1    | Problem-solving search and how to measure performance.\\ Iterative deepening and other uninformed search methods.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/04 Measuring Search Performance.ipynb|04 Measuring Search Performance]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/05 Iterative Deepening and Other Uninformed Search Methods.ipynb|05 Iterative Deepening and Other Uninformed Search Methods]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/06 Python Implementation of Iterative Deepening.ipynb|06 Python Implementation of Iterative Deepening]]    | Sections 3.1 - 3.4  |   |
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 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
 | Week 3:\\ Sept 4 - Sept 8  | Informed search. A* search. Python classes, sorting, numpy arrays.  |  | Rest of Chapter 3  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A1 Uninformed Search.ipynb|A1 Uninformed Search]] due Tuesday, September 5th, at 10:00 PM. |
-| Week 4:\\ Sept 11 - Sept 15   |  A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization.  |  | Chapter 4  |
+| Week 4:\\ Sept 11 - Sept 15   | A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization.  |  | Chapter 4  |
 | Week 5:\\ Sept 18 - Sept 22   | Adversarial search. Minimax. Alpha-beta pruning. Negamax, with pruning.  |  | Chapter 5  |
 | Week 6:\\ Sept 25 - Sept 29   | Stochastic games. Expectimax. |  | Sections 5.5 - 5.6 |

CS440: Introduction to Artificial Intelligence

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