Course Schedule

 

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

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

22-May Final Project Due