/***

To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:

  export PATH=/usr/local/anaconda3/latest/bin:$PATH

***/

/***
Please send your suggestions regarding lecture topics to Chuck using [[https://tinyurl.com/2nyfzc36|this Google Docs form]].  Questions regarding assignments should be entered in Canvas discussions.
***/
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The following schedule is **tentative and is being updated**.


===== August =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| Week 1:\\  Aug 20, 22   | Course overview. Jupyter notebooks.    |  | [[https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html|JupyterLab Introduction]], watch the video then play with jupyter lab.  \\ [[https://tinyurl.com/2qw45tlp|The Batch]] from DeepLearning.AI. Yay, Colorado!  \\  [[https://www.freecodecamp.org/news/exploratory-data-analysis-with-numpy-pandas-matplotlib-seaborn/|What is Data Analysis? How to Visualize Data with Python, Numpy, Pandas, Matplotlib & Seaborn Tutorial]], by Aakash NS| Not graded: Please fill out [[https://forms.gle/hppJ5QuRFuRn1L2h7|this anonymous survey]] before Thursday class.  |
| Week 2:\\  Aug 27, 29  | Jupyter notebook animations. Optimization algorithms. Simple linear and nonlinear models.   |   |   |  |

===== September =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| Week 3:\\  Sept 3, 5\\ Chuck's office hours Thursday will be from 2 to 3:30.  | Confidence intervals. Introduction to neural networks.  |  | |  |
| Week 4:\\  Sept 10, 12   | Design of NeuralNetwork class. Optimizers.  |  | [[https://machinelearningmastery.com/weight-initialization-for-deep-learning-neural-networks/|Weight Initialization for Deep Learning Neural Networks]], by Jason Brownlee  |
| Week 5:\\  Sept 17, 19  | Using optimizers.  |   | |   |
| Week 6:\\  Sept 24, 26  | Early stopping (new version of optimizers). A3. Introduction to classification.   |   |

===== October =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| Week 7:\\  Oct 1, 3  | Classification with QDA, LDA, and linear logistic regression.  |  | |  |
| Week 8:\\  Oct 8, 10  | Classification with Nonlinear Logistic Regression. Introduction to Reinforcement Learning.  |  | |
| Week 9:\\  Oct 15, 17  | Reinforcement learning with Q Function as Neural Network. Learning to play games. |   | [[https://lastweekin.ai/p/241|Last Week in AI]]\\ [[https://www.cbsnews.com/news/geoffrey-hinton-ai-dangers-60-minutes-transcript/?utm_source=substack&utm_medium=email|Geoffrey Hinton: AI Dangers, on 60 Minutes]]  |    |
| Week 10:\\  Oct 22, 24  | Modular framework for reinforcement learning. Convolutional Neural Networks.   |   | |   |
| Week 11:\\  Oct 29, 31  | Ray. Pytorch.  Convolutional Neural Networks.  |   | [[https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/|President Biden's Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence]]  |   |

===== November =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| Week 12:\\  Nov 5, 7  | Convolutional Neural Networks. Ensembles.  |  | |
| Week 13:\\  Nov 12, 14  | Clustering. K-Nearest Neighbors. Jax.  |   |   |
| Week 14:\\  Nov 19, 21  | Support Vector Machines. Web Apps with Streamlit. Word Embeddings.  |   | [[https://www.nature.com/articles/d41586-023-03635-w|ChatGPT generates fake data set to support scientific hypothesis]]  |   |
| Fall Break:\\ Nov 25-29 | No classes.  |

===== December =====

|< 100% 18% 20% 22% 20% 20%  >|
^  Week      ^  Topic      ^  Lecture Notes  ^  Reading          ^  Assignments  ^
| Week 15:\\  Dec 3, 5  | Transformers.  |   |     | |
| Dec 10-12  |  Final Exam Week  |  No Exams in this course  | |   |