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syllabus rework
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feamster committed Jun 24, 2024
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Expand Up @@ -43,8 +43,8 @@ More details are in the [course syllabus](syllabus.md).
| **Machine Learning Pipeline** | | | |
| 7 | Data Preparation and Representation<br>([Data Preparation](notebooks/07-Data-Preparation-Clean.html)) | [Ch. 4.1 ](https://noise-lab.github.io/ml-systems/book/text/pipelines.html#data-preparation) | |
| 8 | Model Training and Evaluation<br>([Model Evaluation](notebooks/08-ML-Pipeline-Clean.html)) | [Ch. 4.2-4.3](https://noise-lab.github.io/ml-systems/book/text/pipelines.html#model-training) | |
| **Supervised Learning** | | | |
| 9 | Non-Parametric and Probabilistic Models<br>([Naive Bayes](notebooks/09-Naive-Bayes-Clean.html)) | [Ch. 5](book/text/supervised.html) | |
| **Supervised Learning** | | [Ch. 5](book/text/supervised.html) | |
| 9 | Non-Parametric and Probabilistic Models<br>([Naive Bayes](notebooks/09-Naive-Bayes-Clean.html)) | | |
| 10 | Linear and Polynomial Regression<br>([Linear Regression](notebooks/10-Linear-Regression-Clean.html)) | | |
| 11 | Logistic Regression and Support Vector Machines<br>([Logistic Regression](notebooks/11-Logistic-Regression-Clean.html)) | | |
| 12 | Trees and Ensembles<br>([Trees and Ensembles](notebooks/12-Trees-Ensembles-Clean.html)) | | [Midterm/Take-Home](assignments/midterm.html) |
Expand All @@ -53,12 +53,13 @@ More details are in the [course syllabus](syllabus.md).
| 14 | Dimensionality Reduction<br>([Dimensionality Reduction](notebooks/14-Dimensionality-Reduction-Clean.html)) | | |
| 15 | Clustering<br>([Clustering](notebooks/15-Clustering-Clean.html)) | | Anomaly Detection |
| 16 | Autoencoders and Reinforcement Learning<br>([Autoencoders](notebooks/16-Autoencoders-Clean.html)) |
| **Cutting Edge Techniques** | | | |
| 17 | Large-Language Models <br>([Large Language Models](notebooks/18-LLM-Clean.html)) | [Ch. 7.](book/text/llm.html) | |
| 18 | Reinforcement Learning<br>([Automation](notebooks/19-Reinforcement-Clean.html)) | [Ch. 8](book/text/reinforcement.html) | |
| **Generative Models** | | [Ch. 7](book/text/llm.html) | |
| 17 | Transformers <br>([Large Language Models](notebooks/18-LLM-Clean.html)) | | |
| 18 | Diffusion Denoising | | Performance Monitoring |
| **Bonus Topics** | | | |
| 19 | Automation<br>([Automation](notebooks/16-Automation-Clean.html)) | | | [Ch. 9](book/text/deployment.html)
| 20 | Timeseries Analysis<br>([Timeseries](notebooks/17-Timeseries-Clean.html)) | | Performance Monitoring |
| 19 | Reinforcement Learning<br>([Automation](notebooks/19-Reinforcement-Clean.html)) | [Ch. 8](book/text/reinforcement.html) | |
| 20 | Automation<br>([Automation](notebooks/16-Automation-Clean.html)) | [Ch. 9](book/text/deployment.html) |
| 21 | Timeseries Analysis<br>([Timeseries](notebooks/17-Timeseries-Clean.html)) | | |

Please come to class having done the reading.

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