CS440: Introduction to Artificial Intelligence

User Tools

Site Tools

Trace:
start

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision 		Previous revision
Next revision Both sides next revision
start [2017/02/27 15:58]
127.0.0.1 external edit 		start [2017/09/05 10:56]
127.0.0.1 external edit
Line 1: Line 1:
 ====== Schedule ====== ====== Schedule ======
 +
 +===== Announcements =====
 +
 +
 +Aug 31: Assignment 1 now includes another example.
 +
 +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
  
 /* /*
-Follow this link to view all [[https://echo.colostate.edu/ess/portal/section/37e +are available at this [[https://echo.colostate.edu/ess/portal/section/a5759ae3-82dc-43df-b515-dd944a6c4976|CS480 video recordings site]].
-115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]].+
 */ */
-===== Announcements ===== 
  
-**Feb 19:** The links in A3 for nnA3.tar and A3grader.tar now work. 
  
-Lecture videos are available at this [[https://echo.colostate.edu/ess/portal/section/a5759ae3-82dc-43df-b515-dd944a6c4976|CS480 video recordings site]].+===== August =====
  
 +|< 100% 10% 20% 30% 20% 20%  >|
 +^  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.   | [[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  |   | 
  
-===== January =====+ 
 +===== September =====
  
 |< 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 17 - Jan 20    | Overview. Intro to machine learning. Python.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/01 Course Overview.ipynb|01 Course Overview]],\\  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/02 Matrices and Plotting.ipynb|02 Matrices and Plotting]],  | [[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.\\ Section 1 of   [[http://www.scipy-lectures.org|Scipy Lecture Notes]]      |  |  +| Week 3:\\ Sept 4 - Sept 8  | Informed search. A* search. Python classes, sorting, numpy arrays.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/07 Informed Search.ipynb|07 Informed Search]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/08 Python Classes.ipynb|08 Python Classes]]\\  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/09 Heuristic Functions.ipynb|09 Heuristic Functions]]   | 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 2:\\ Jan 23 - Jan 27    | Probability distributions and regression.    | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/03 Linear Regression.ipynb|03 Linear Regression]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/04 Gaussian Distributions.ipynb|04 Gaussian Distributions]]    |   |   +| Week 4:\\ Sept 11 - Sept 15   | A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization.  |  | Chapter 4  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A2 Iterative-Deepening Search.ipynb|A2 Iterative-Deepening Search]] due Tuesday, September 12th, at 10:00 PM.   | 
 +| 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 |
  
 +===== October =====
  
-===== February =====+|< 100% 10% 20% 30% 20% 20%  >| 
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ 
 +| Week 7:\\ Oct 2 - Oct 6  |  
 +| Week 8:\\ Oct 9 - Oct 13  |  
 +| Week 9:\\ Oct 16 - Oct 20  |  
 +| Week 10:\\ Oct 23 - Oct 27  | Introduction to Reinforcement Learning.  |  | Chapter 21\\ [[http://incompleteideas.net/sutton/book/the-book-2nd.html|Reinforcement Learning: An Introduction]]  |  | 
 + 
 +===== November =====
  
 |< 100% 10% 20% 30% 20% 20%  >| |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 3:\\ Jan 30 - Feb 3      | Probabilistic Linear Regression. Ridge regression. Data partitioning. On-line, incremental regression.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/05 Fitting Gaussians.ipynb|05 Fitting Gaussians]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/06 Probabilistic Linear Regression.ipynb|06 Probabilistic Linear Regression]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/07 Linear Ridge Regression and Data Partitioning.ipynb|07 Linear Ridge Regression and Data Partitioning]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/08 Sample-by-Sample Linear Regression.ipynb|08 Sample-by-Sample Linear Regression]]    | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1 Linear Regression.ipynb|A1 Linear Regression]] due Monday, January 30th at 10:00 PM.   |    +| Week 11:\\ Oct 30 - Nov 3  |   |  |  | 
-| Week 4:\\ Feb 6 - Feb 10   | Regression with fixed nonlinearities. Nonlinear regression with neural networks.\\ Feb 10: Guest Speaker [[https://www.linkedin.com/in/mike-morain-07223710|Mike Morain]], Machine Learning at Amazon, UK.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/09 Linear Regression with Fixed Nonlinear Features.ipynb|09 Linear Regression with Fixed Nonlinear Features]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/10 Nonlinear Regression with Neural Networks.ipynb|10 Nonlinear Regression with Neural Networks]]   | |   |    +| Week 12:\\ Nov 6 - Nov 10  |  |  |  | 
-| Week 5:\\ Feb 13 - Feb 17   | Neural Networks  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/10 Nonlinear Regression with Neural Networks.ipynb|10 Nonlinear Regression with Neural Networks]],\\  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/11 More Nonlinear Regression with Neural Networks.ipynb|11 More Nonlinear Regression with Neural Networks]]   | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2 Ridge Regression with K-Fold Cross-Validation.ipynb|A2 Ridge Regression with K-Fold Cross-Validation]] due Monday, February 13th at 10:00 PM.\\ Here are [[A2-good-ones | examples of good A2 reports.]]  | +| Week 13:\\ Nov 13 - Nov 17  |  |  |  | 
-| Week 6:\\ Feb 20 - Feb 24   | Neural Networks. Autoencoders. Guest lectures by our GTA, Jake Lee.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/12 Autoencoder Neural Networks.ipynb|12 Autoencoder Neural Networks]]   | |   |     +|  Nov 20 - Nov 24  |  Fall Break  | 
-| Week 7:\\ Feb 27 - Mar 3   | Recurrent Neural Networks.  | \\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/13 Recurrent Neural Networks.ipynb|13 Recurrent Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/14 Introduction to Classification.ipynb|14 Introduction to Classification]]   | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3 Neural Network Regression.ipynb|A3 Neural Network Regression]] due Wednesday, March 1st at 10:00 PM.  |    +| Week 14:\\ Nov 27 - Dec 1  | Constraint satisfaction. Min-conflicts  |  | Chapter 6.\\ [[http://dl.acm.org/citation.cfm?id=1928809|A new iterated local search algorithm for solving broadcast scheduling problems in packet radio networks]]  |  
 + 
 +===== December ===== 
 + 
 +|< 100% 10% 20% 30% 20% 20%  >| 
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ 
 +| Week 15:\\ Dec 4 - Dec 8  | Propositional and First-Order Logic. Introduction to Prolog.  |  | Chapters 7, 8, 9  |  
 +| Finals Week:\\ Dec 11 - Dec 15  |   |  |  | 
  
  
start.txt · Last modified: 2024/01/08 18:40 (external edit)

Page Tools

Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Noncommercial-Share Alike 4.0 International
CC Attribution-Noncommercial-Share Alike 4.0 International Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki