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 [2018/11/07 17:36]
127.0.0.1 external edit 		start [2018/12/03 07:21]
anderson [December]
Line 46: Line 46:
 | Week 11:\\ Oct 29 - Nov 2  | Classification with Neural Networks. Reinforcement Learning with Neural Networks.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/19 Classification with Linear Logistic Regression.ipynb|19 Classification with Linear Logistic Regression]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/20 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|20 Classification with Nonlinear Logistic Regression Using Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/21 Reinforcement Learning with a Neural Network as the Q Function.ipynb|21 Reinforcement Learning with a Neural Network as the Q Function]]  | |   | | Week 11:\\ Oct 29 - Nov 2  | Classification with Neural Networks. Reinforcement Learning with Neural Networks.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/19 Classification with Linear Logistic Regression.ipynb|19 Classification with Linear Logistic Regression]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/20 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|20 Classification with Nonlinear Logistic Regression Using Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/21 Reinforcement Learning with a Neural Network as the Q Function.ipynb|21 Reinforcement Learning with a Neural Network as the Q Function]]  | |   |
 | Week 12:\\ Nov 5 - Nov 9  | Introduction to Pytorch.\\ Constraint satisfaction.\\ Min-conflicts.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/23 Introduction to Pytorch.ipynb|23 Introduction to Pytorch]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/24 Constraint Satisfaction Problems.ipynb|24 Constraint Satisfaction Problems]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/25 Min-Conflicts in Python with Examples.ipynb|25 Min-Conflicts in Python with Examples]]  | 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]]  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A5 Neural Networks.ipynb|A5 Neural Networks]] due Monday, Nov. 5, 10:00 PM.\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/Project Proposal.ipynb|Project Proposal]] due Friday, November 9th, at 10:00 PM.   | | Week 12:\\ Nov 5 - Nov 9  | Introduction to Pytorch.\\ Constraint satisfaction.\\ Min-conflicts.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/23 Introduction to Pytorch.ipynb|23 Introduction to Pytorch]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/24 Constraint Satisfaction Problems.ipynb|24 Constraint Satisfaction Problems]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/25 Min-Conflicts in Python with Examples.ipynb|25 Min-Conflicts in Python with Examples]]  | 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]]  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A5 Neural Networks.ipynb|A5 Neural Networks]] due Monday, Nov. 5, 10:00 PM.\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/Project Proposal.ipynb|Project Proposal]] due Friday, November 9th, at 10:00 PM.   |
-| Week 13:\\ Nov 12 - Nov 16  | Faster Reinforcement Learning.  |   |  |  |+| Week 13:\\ Nov 12 - Nov 16  | Natural language understanding and translation.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/26 Natural Language.ipynb|26 Natural Language]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/27 Word Embeddings.ipynb|27 Word Embeddings]]  | [[https://towardsdatascience.com/word-embedding-with-word2vec-and-fasttext-a209c1d3e12c|Word2Vec and FastText Word Embedding with Gensim]]  |  |
 |  Nov 19 - Nov 23  |  Fall Recess  | |  Nov 19 - Nov 23  |  Fall Recess  |
-| Week 14:\\ Nov 26 - Nov 30  |   |   |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A6 Min-Conflicts.ipynb|A6 Min-Conflicts]] due Monday, Nov. 26, 10:00 PM.   | +| Week 14:\\ Nov 26 - Nov 30  | Faster Reinforcement Learning   | [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/15ijcnn.pdf|Slides for Faster Reinforcement Learning After Pretraining]]  | [[http://www.cs.colostate.edu/~anderson/res/rl/pretrainijcnn15.pdf|Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics]] by Anderson, Lee and Elliott  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A6 Min-Conflicts.ipynb|A6 Min-Conflicts]] due Wednesday, Nov. 28, 10:00 PM.   | 
  
 ===== December ===== ===== December =====
Line 54: Line 54:
 |< 100% 10% 20% 30% 20% 20%  >| |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 15:\\ Dec 3 - Dec 7  | Recurrent neural networks and use in natural language  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/25 Natural Language.ipynb|25 Natural Language]] |   |  +| Week 15:\\ Dec 3 - Dec 7  | Voluntary in-class project presentations.  | **Dec 3:**\\ Tom Cavey: //Image Classification and Object Detection of things around CSU\//\ Jason Stock: Classification of Data from the Sloan Digital Sky Survey\\ Marylou Nash: Physical Routing on ICs or PCBs with A*\\ \\ Dec 5:\\ Jake Walker: Legal, Ethical, and Security Concerns for Autonomous Driving Technologies\\ Andy Dolan: Using Machine Learning Methods to Classify BGP Messages\\ Miles Wood: Using Q-Learning to Learn to Play Chad, a Chess Variant\\ Apoorv Pandey: Using Q-Learning to Learn to Play 2x2 Dots and Boxes  |   |  
-| Final Exam Week:\\ Dec 10 - Dec 14  |   |  | | Final Project notebook is due Tuesday, Dec 11th, 10:00 pm.   |+| Final Exam Week:\\ Dec 10 - Dec 14  |   |  | | Final Project notebook is due Tuesday, Dec 11th, 10:00 pm. Here is an [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/Example of Project Report.ipynb|notebook explaining what is expected]] for your final report.   |
  
  
  
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