| 29-Jan |
Introduction |
| 5-Feb |
What is Machine Learning?
Readings:
[1] Ch 1: “The Machine Learning Landscape” in Géron, Aurélien. (2019). Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow’ O’Reilly Media, Inc. 3–31. https://www.lpsm.paris/pageperso/has/source/Hand-on-ML.pdf
[2] Jordan, Michael I. and Tom M. Mitchell. (2015). “Machine Learning: Trends, perspectives, and prospects” Science 349, 255—-60. http://www-cgi.cs.cmu.edu/~tom/pubs/Science-ML-2015.pdf
|
| 12-Feb |
No Class |
| 19-Feb |
Getting Started with Machine Learning
Readings:
Ch 1: “Introduction” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 1–25.
|
| 26-Feb |
Inspecting Data
Readings:
Ch 2: End-to-End Machine Learning Project. in Géron, Aurélien. (2019). Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow’ O’Reilly Media, Inc. 33–66. https://www.lpsm.paris/pageperso/has/source/Hand-on-ML.pdf.
|
| 4-Mar |
Representing Data
Readings:
Ch 4: “Representing Data/Engineering Features” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 213–55
|
| 11-Mar |
Evaluation Methods
Readings:
Ch 5: “Model Evaluation and Improvement” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 213–55
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| 18-Mar |
No Class |
| 25-Mar |
Supervised Learning (k-Nearest Neighbors)
Project 1 DueReadings:
Ch 2: “Supervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 27–46
DataCamp:
(1) “Basic Modeling in scikit-learn (through Feature Importances)” (In Model Validation in Python course)
(2) “Classification” (in Supervised Learning with scikit-learn course)
Lecture Slides and Jupyter Notebook:
https://github.com/jcdevaney/data71200sp20/tree/master/class7
Lecture Video:
password is course number (no spaces)
|
| 1-Apr |
Supervised Learning (Linear Models)
Readings:
Ch 2: “Supervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 47–70
DataCamp:
(1) “Basic Modeling in scikit-learn (through Feature Importances)” (In Model Validation in Python course)
(2) “Regression” (in Supervised Learning with scikit-learn course)
Lecture Slides and Jupyter Notebook:
https://github.com/jcdevaney/data71200sp20/tree/master/class8
Lecture Video:
password is course number (no spaces)
|
| 7-Apr |
Supervised Learning (Naive Bayes Classifiers and Decision Trees)
Readings:
Ch 2: “Supervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 70–94
DataCamp:
(1) “Classification and Regression Trees” (in Machine Learning with Tree-Based Models in Python)
Lecture Slides and Jupyter Notebook:
https://github.com/jcdevaney/data71200sp20/tree/master/class9
Lecture Video:
password is course number (no spaces)
|
| 8-Apr |
No class |
| 15-Apr |
No class |
| 22-Apr |
Supervised Learning (Support Vector Machines and Uncertainty estimates from Classifiers)
Readings:
Ch 2: “Supervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 93–106 and 121–131
DataCamp:
(1) “Applying logistic regression and SVM” (in Linear Classifiers in Python course)
(2) “Loss functions” (in Linear Classifiers in Python course)
(3) “Logistic regression” (in Linear Classifiers in Python course)
(4) “Support Vector Machines” (in Linear Classifiers in Python course)
Lecture Slides and Jupyter Notebook:
https://github.com/jcdevaney/data71200sp20/tree/master/class10
Lecture Video:
password is course number (no spaces)
|
| 29-Apr |
Unsupervised Learning (Dimensionality Reduction & Feature Extraction, and Manifold Learning)
Project 2 Due
Readings:
Ch 3: “Unsupervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 133–170
DataCamp:
(1) “Clustering for dataset exploration” (in Unsupervised Learning in Python course)
(2) “Visualization with hierarchical clustering and t-SNE” (in Unsupervised Learning in Python course)
Lecture Slides and Jupyter Notebook:
https://github.com/jcdevaney/data71200sp20/tree/master/class11
Lecture Video:
password is course number (no spaces)
|
| 6-May |
Unsupervised Learning (Clustering)
Readings:
Ch 3: “Unsupervised Learning” in Guido, Sarah and Andreas C. Muller. (2016). Introduction to Machine Learning with Python, O’Reilly Media, Inc. 170–211
DataCamp:
(1) “Decorrelating your data and dimension reduction“ (in Unsupervised Learning in Python course)
(2) “Discovering interpretable features” (in Unsupervised Learning in Python course)
Lecture Slides and Jupyter Notebook:
https://github.com/jcdevaney/data71200sp20/tree/master/class12
Lecture Video:
password is course number (no spaces)
|
| 13-May |
Ethics
Project 3 Due
Readings:
Bostrom, Nick, and Eliezer Yudkowsky. (2014). “The ethics of artificial intelligence.” The Cambridge Handbook of Artificial Intelligence. 316–34. http://faculty.smcm.edu/acjamieson/s13/artificialintelligence.pdf
West, Sarah Myers, Meredith Whittaker, and Kate Crawford. (2019). “Discriminating systems: Gender, race and power in AI.” AI Now Institute, 1–33. https://ainowinstitute.org/discriminatingsystems.pdf
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| 22-May |
Final Project Due |
