CS 470 - Spring 2020.
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Welcome to CS 470!
Video of the Day
Lieder ohne Worte (Songs without Words), Mendelssohn. including "Spring Song"
Poll of the Day
Psychology has studied learning. In cognitive psychology, you can learn
from success or from failure. Based on your personal experience, which is more
effective?
https://pollev.com/cs470
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Coronavirus
COVID-19 Global Cases by Johns Hopkins CSSE
Lecture : Learning. 4/3/2020
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.
Google COVID-19 dataset
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.
Toward ML-Centric Cloud Platforms CACM February, 2020. Microsoft Azure guys. Focus
is on the engineering aspects of the cloud, not the content. Sigh.
- There are many potential uses of ML in
cloud computing platforms. The challenge
is in defining exactly how and where ML
should be infused in these platforms.
- Leveraging ML-derived predictions
has shown promise for many resource
managers in Azure Compute. Having a
general and independent ML framework/
system has been key to increasing
adoption quickly.
- Many research challenges remain
open, including how to make action prescribing ML general enough for wide
applicability in cloud platforms, how to
manage (potentially partial) feedback at
scale, and how to debug misbehaviors
(especially when the ML is tightly
integrated with resource managers).
0403.ipynb
0403.html Naive Bayes, Perceptron, Linear, Adaboost.
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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