CS440: Introduction to Artificial Intelligence

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schedule [2016/02/22 16:19]
anderson [Announcements] 		schedule [2024/01/08 18:40] (current)
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-====== Schedule ====== +===== Schedule ======
- +
-Follow this link to view all [[https://echo.colostate.edu/ess/portal/section/37e115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]].+
  
 ===== Announcements ===== ===== Announcements =====
  
-  * Feb 22: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3 Neural Network Regression.ipynb|A3 Neural Network Regression]], now includes link to ''A3grader.tar'' that contains ''A3grader.py''.+Links to live MS Teams events: 
 +  * Lectures: [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598126257845?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Tuesdays/Thursdays, 2:00 - 3:15 PM]] 
 +  * Office hours: Apoorv [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598300599034?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Mondays, 2:00 - 4:00 PM]] 
 +  * Office hours: Chaitanya [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598301087268?|Fridays, 2:00 - 4:00 PM]] 
 +  * Office hours: Chuck [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598288070646?|Wednesdays, 9:00 - 10:00 AM]] 
 + 
  
-===== January =====+Recordings of lecture and office hour videos are available from the Home page of our  
 +[[https://colostate.instructure.com/courses/109411|Canvas site]].
  
-|< 100% 20% 20% 30% 10% 20%  >| +To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ +
-| Week 1:\\  Jan 19 - Jan 22    | 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]],  | Text: Sections 1.1-1.5. Section 1 of   [[http://www.scipy-lectures.org|Scipy Lecture Notes]]      |  |  +
-| Week 2:\\ Jan 25 - Jan 29    | 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]],\\ [[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]]    | Sections 4.1-4.2, 4.6-4.9, 5.8-5.9     |  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1 Linear Regression.ipynb|A1 Linear Regression]] due Friday, January 29th at 10:00 PM. Download and unzip [[http://www.cs.colostate.edu/~anderson/cs480/notebooks/A1 Grader.zip|A1 Grader.zip]]\\ Here are five examples of good solutions: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1a.ipynb|A1a]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1b.ipynb|A1b]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1c.ipynb|A1c]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1d.ipynb|A1d]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1e.ipynb|A1e]]   |  +
  
-===== February =====+  export PATH=/usr/local/anaconda/bin:$PATH
  
-|< 100% 20% 20% 30% 10% 20%  >|+This is a tentative schedule of CS440 topics for Fall, 2020.  This will be updated during the summer and as the fall semester continues. 
 + 
 + 
 +===== August ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 3:\\ Feb 1 - Feb 5      | Ridge regression. Data partitioning. On-line, incremental regression. Regression with fixed nonlinearities.  |  [[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/09 Linear Regression with Fixed Nonlinear Features.ipynb|09 Linear Regression with Fixed Nonlinear Features]]    | | +| Week 1:\\  Aug 24 - Aug 28    | What is AI?  Promises and fears.\\ Python review.\\ Problem-Solving Agents.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/01 Introduction to AI.ipynb|01 Introduction to AI]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/02 Introduction to Python.ipynb|02 Introduction to Python]]   | Chapters 1, 2, 3.1 of Russell and Norvig.\\ Section 1 of [[http://www.scipy-lectures.org|Scipy Lecture Notes]]  \\ [[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\\ <!-- [[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\\ -->    |  | 
-| Week 4:\\ Feb 8 - Feb 12     | Nonlinear regression with 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]]  | 11.1-11.5, 11.7.1, 11.7.4, 11.8.1-11.8.2  |  +
-| Week 5:\\ Feb 15 - Feb 19    | Autoencoders. Recurrent neural networks.     |   [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/12 Autoencoder Neural Networks.ipynb|12 Autoencoder Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/13 Recurrent Neural Networks.ipynb|13 Recurrent Neural Networks]]   | 11.9, 11.12, 11.14   |  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2 Linear Regression with Fixed Nonlinear Features.ipynb|A2 Linear Regression with Fixed Nonlinear Features]] due Monday, Feb 15 at 10:00 PM.   | +
-| Week 6:\\ Feb 22 - Feb 26    | Classification, generative models.   |  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/14 Introduction to Classification.ipynb|14 Introduction to Classification]]   | 4.3-4.5, 5.5-5.7  |+
  
-===== March =====+===== September =====
  
-|< 100% 20% 20% 30% 10% 20%  >|+|< 100% 18% 20% 22% 20% 20%  >| 
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^nderson/cs545/doku.php?id=schedule#september 
 +| Week 2:\\ Aug 31 - Sept 4    | Help with A1.\\ Problem-solving search and how to measure performance.\\ Iterative deepening and other uninformed search methods.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/03 Problem-Solving Agents.ipynb|03 Problem-Solving Agents]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/04 Measuring Search Performance.ipynb|04 Measuring Search Performance]]\\ [[https://nbviewer.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]]   | Sections 3.1 - 3.4 of Russell and Norvig  |   |  
 +| Week 3:\\ Sept 7 - Sept 11  | Informed search. A* search. Python classes, sorting, numpy arrays.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/06 Python Implementation of Iterative Deepening.ipynb|06 Python Implementation of Iterative Deepening]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/07 Informed Search.ipynb|07 Informed Search]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/08 Python Classes.ipynb|08 Python Classes]]   | Rest of Chapter 3  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A1.1 Uninformed Search.ipynb|A1.1 Uninformed Search]] due Tuesday, Sept. 8, 10:00 PM.  Submit your notebook in Canvas.\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]  | 
 +| Week 4:\\ Sept 14 - Sept 18   | A* optimality, admissible heuristics  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/09 Heuristic Functions.ipynb|09 Heuristic Functions]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/10 Local Search.ipynb|10 Local Search]]   | Chapter 4  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A2.1 Iterative-Deepening Search.ipynb|A2.1 Iterative-Deepening Search]] due Tuesday, Sept. 15, 10:00 PM.\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]  | 
 +| Week 5:\\ Sept 21 - Sept 25   | Effective branching factor.\\ Local search and optimization. Adversarial search. Minimax. Alpha-beta pruning. Stochastic games.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/11 Adversarial Search.ipynb|11 Adversarial Search]]   | Chapter 5  | 
 +| Week 6:\\ Sept 28 - Oct 2   | Negamax, with pruning. Introduction to Reinforcement Learning.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/12 Negamax.ipynb|12 Negamax]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/13 Modern Game Playing.ipynb|13 Modern Game Playing]]\\  [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/14 Introduction to Reinforcement Learning.ipynb|14 Introduction to Reinforcement Learning]]      | Chapter 21\\ [[http://incompleteideas.net/book/bookdraft2017nov5.pdf|Reinforcement Learning: An Introduction]]   |   [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A3 A*, IDS, and Effective Branching Factor.ipynb|A3 A*, IDS, and Effective Branching Factor]] due Wednesday, Sept. 30, 10:00 PM.\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]  | 
 + 
 +===== October ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 7:\\ Feb 29 - Mar 5     | Classification, discriminant models.  Ranking.  | | 10.1-10.4, 10.5-10.10    |  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3 Neural Network Regression.ipynb|A3 Neural Network Regression]] due Monday, Feb 29 at 10:00 PM.  | +| Week 7:\\ Oct 5 - Oct 9\\ <color red>Oct 8 Lecture will not meet, but recording will be available.</color>  | Reinforcement Learning for Two-Player Games.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/15 Reinforcement Learning for Two-Player Games.ipynb|15 Reinforcement Learning for Two-Player Games]]   | Chapter 21\\ [[http://incompleteideas.net/book/bookdraft2017nov5.pdf|Reinforcement Learning: An Introduction]]  |  | 
-| Week 8:\\ Mar 7 - Mar 11     | Classification with neural networks.     | | 11.7.2     | +| Week 8:\\ Oct 12 - Oct 16  | Constraint satisfaction.\\ Min-conflicts.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/16 Constraint Satisfaction Problems.ipynb|16 Constraint Satisfaction Problems]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/17 Min-Conflicts.ipynb|17 Min-Conflicts]]  <!-- \\ [[https://nbviewer.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  | 
-|  Mar 14 - Mar 18    | Spring Break!    |       | +| Week 9:\\ Oct 19 - Oct 23  | Natural language processing.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/18 Introduction to Natural Language Processing.ipynb|18 Introduction to Natural Language Processing]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/19 More NLP.ipynb|19 More NLP]] <!-- \\ [[https://nbviewer.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]]  |  [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A4 Reinforcement Learning Solution To Towers of Hanoi.ipynb|A4 Reinforcement Learning Solution To Towers of Hanoi]] due Tuesday, Oct. 20, 10:00 PM. Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]  | 
-| Week 9:\\ Mar 21 - Mar 25    | Convolutional, bottleneck, and deep networks.    | | 11.8.3, 11.11, 11.13     |  +| Week 10:\\ Oct 26 - Oct 30  | Introduction to Neural Networks  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/20 Introduction to Neural Networks.ipynb|20 Introduction to Neural Networks]]\\  [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/21 Pytorch Neural Networks.ipynb|21 Pytorch Neural Networks]]   <!-- \\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/17 More Introduction to Neural Networks.ipynb|17 More Introduction to Neural Networks]]  -->  | Sections 18.6 and 18.7    | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A5.1 Min-Conflicts.ipynb|A5.1 Min-Conflicts]] due Friday Oct 30, 10:00 PM. Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]   |
-| Week 10:\\ Mar 28 - Apr 1    | Nonparametric methods.  | | 8.1-8.10  |+
  
-===== April ===== 
  
-|< 100% 20% 20% 30% 10% 20%  >|+===== November ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 11:\\ Apr 4 - Apr 8      | Dimensionality reduction.  | | 6.1-6.8, 6.10-6.13  | +| Week 11:\\ Nov 2 - Nov 6  | More Neural Networks  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/22 Classification with Pytorch Neural Networks.ipynb|22 Classification with Pytorch Neural Networks]]  <!-- [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/17 More Introduction to Neural Networks.ipynb|17 More Introduction to Neural Networks]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/22 Autoencoder Neural Networks.ipynb|22 Autoencoder Neural Networks]] -->  |    |     | 
-| Week 12:\\ Apr 11 - Apr 15    | Clustering  | | 7.1-7.10  |   +| Week 12:\\ Nov 9 - Nov 13  | Interpreting what a neural network has learned.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/23 Interpreting What a Neural Network Has Learned.ipynb|23 Interpreting What a Neural Network Has Learned]]   <!-- [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/18 Introduction to Classification.ipynb|18 Introduction to Classification]] -->  |     | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A6 Neural Networks.ipynb|A6 Neural Networks]] due <color red>Sunday Nov 15, 10:00 PM.</color>\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]   | 
-| Week 13:\\ Apr 18 - Apr 22    | Support vector machines.   | | 13.1-13.12   | +| Week 13:\\ Nov 16 - Nov 20  | Natural language processing with neural nets.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/24 NLP With Transformers.ipynb|24 NLP With Transformers]]  <!-- [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/19 Classification with Linear Logistic Regression.ipynb|19 Classification with Linear Logistic Regression]]\\ [[https://nbviewer.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]]\\ [[https://nbviewer.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 14:\\ Apr 25 - Apr 29    | Reinforcement learning.   | | 18.1-18.9   |+|  Nov 23 - Nov 27  |  Fall Recess!  |
  
-===== May =====+===== December =====
  
-|< 100% 20% 20% 30% 10% 20%  >|+|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 15:\\ May 2 - May 6    | Multiple models.    | | 17.1-17.12   |+| Week 14:\\ Nov 30 - Dec 4  | Clustering. Word embeddings.\\ Genetic algorithms.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/25 Clustering of Word Embeddings.ipynb|25 Clustering of Word Embeddings]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/26 Genetic Algorithm Search.ipynb|26 Genetic Algorithm Search]]   |  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A7.1 NLP with Transformers and the T5 Model.ipynb|A7.1 NLP with Transformers and the T5 Model]] due Sunday, Dec 6, 10:00 PM\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]   |  
 +| Week 15:\\ Dec 7 - Dec 11  | Brain-Computer Interfaces. Pre-training for faster reinforcement learning.   |   |   |  
 +| Final Exam Week:\\ Dec 14 - Dec 18  | No exam.    |  | | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A8 Report Template.ipynb|A8 Report Template]] due Tuesday, December 15th, 10:00 PM.   | 
 + 
 + 
 + 
  
schedule.1456183164.txt.gz · Last modified: 2016/02/22 16:19 by anderson

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