CS 470 - Spring 2020.

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Welcome to CS 470!

Video of the Day

Haydn's The Four Seasons: Introduction and Spring

Poll of the Day

What is the best thing that has ever happened to you in the spring? Aside from being admitted to Yale.

https://pollev.com/cs470 You may also download the app to your phone. Use the "cs470" poll id.

Coronavirus COVID-19 Global Cases by Johns Hopkins CSSE

Lecture : Learning. 4/1/2020

Administrivia

Zoom Guide for Yale Students Sign in using Yale zoom account.

I have office hours Wednesdays from 4-6 pm, via zoom, standard meeting id: 316-021-726

ULA office hours - also on zoom. See the [Contact Info and Schedule] as well as piazza. The zoom office hours meetings are posted on canvas, under zoom. If you have trouble connecting, post a note to piazza or email cs470help@cs.yale.edu.

[Assignments]. hw6 has been posted.

There was an issue with embedded commas in the name field of the .csv files. The parse_csv() function did not handle this correctly. I have edited the .csv files to remove the offending commas. You should use the revised files. Note: the prediction results from the learning algorithms did not change much after fixing the commas. I would expect that it would make a difference when running predictions on the test data.
If you manually update fields like age, you may edit the .csv fils and submit those files along with hw6.ipynb file.
You may use other python modules, like numpy or pandas, to modify the data. As someone pointed out, the aima code does not use the dataframe format. Therefore, you can convert the titanic.examples list to the desired format, manipulate the data, and then convert it back to the list format used by titanic.examples.

CS 570 project assignment is also available in [Assignments]. So far three have been submitted and approved.

Final Exam: Friday May 1, 2pm

The final exam will be open book and take home. I will provide more information about the scope in the coming weeks.

Lecture: Learning

Readings: chapters 18-20

2019 Scassellati Slides:

Stuart Russell, Berkeley Slides:

AIMA chapter 18 6up Learning from Observations
AIMA chapter 20a 6up Statistical Learning
AIMA chapter 20b 6up Neural Networks

0304.ipynb 0304.html

0306.ipynb 0306.html

Visualization
distance functions
plurality learner classifier
k-nearest neighbors classifier
decision tree learner
random forest learner

0323.ipynb 0323.html

naive Bayes learner.
Perceptron Classifier
Linear Learner
Ensemble Learner
Learner Evaluation
k-Nearest Neighbor Example
AdaBoost

Machine Learning using scikit learn sklearn.html

sklcompare.py compare various sci kit learn algorithms. Included in above notebook.
Question about white areas in sklcompare plots: Uses RdBu Diverging colormap There is a spectrum from red to Blue, with white in the middle.

learning_apps.ipynb learning_apps.html Continue with MNIST PL.

Modified NIST. Orginal training data from US census and testing data from US high school students. "Re-mixed".

0401.ipynb 0401.html Statistics / Naive Bayes. We (naively) assume that the features are independent to make computations easier.

neural_nets.ipynb neural_nets.html

learning.ipynb learning.html

Jupyter notebook keyboard shortcuts

Lecture: Reinforcement Learning

Readings: chapter 21

2019 Scassellati Slides:

rl.html

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