Readings
- Large Margins Using Perceptron
- https://www.wired.com/2016/06/deep-learning-isnt-dangerous-magic-genie-just-math/
- https://www.nytimes.com/2018/05/18/opinion/artificial-intelligence-challenges.html
- https://arxiv.org/ftp/arxiv/papers/1801/1801.00631.pdf
- http://cogcomp.org/papers/Roth-AAAI17-incidental-supervision.pdf
- Efficient Learning of Linear Perceptrons
EMAB Tutoring
Engineering Master’s Advisory Board (EMAB) has announced Tutoring Program for master’s students in Machine Learning. This program will serve as a resource to help students strengthen their skills in the area. Students can drop in at one of our sessions whenever they need help - no commitment required and free of charge. If a student is interested in this program, they are encouraged to learn about our program at https://pennemab.weebly.com/tutoring.htmlProbability Resources
Linear Algebra Resources
- http://www.seas.upenn.edu/~jadbabai/ESE504/LAreview.pdf
- http://www.cs.cmu.edu/~jingx/docs/linearalgebra.pdf
Python Resources
- Python Development Environment (Anaconda + PyCharm) setup
- Python Numpy Tutorial (Stanford)
- Learn Python on Kaggle
Throughout the course, you may find it useful to consult the following resources:
- Reinforcement Learning: An Introduction by Sutton and Barto, MIT Press, 1998. (Full text available online; on reserve in Penn library)
- Machine Learning by Tom Mitchell, McGraw Hill, 1997. (On reserve in Penn library)
- A Course in Machine Learning by Hal Daumé III.
- Machine Learning Lecture Notes by Andrew Ng.
For a more advanced treatment of machine learning topics, I would recommend one of the following books:
- Pattern Recognition and Machine Learning by Bishop, Springer, 2006. (On reserve in Penn library)
- Machine Learning: A Probabilistic Perspective by Kevin P. Murphy, MIT Press, 2012. (On reserve in Penn library)
- The Elements of Statistical Learning 2nd edition by Hastie, Tibshirani and Friedman, Springer-Verlag, 2008. (Available online)
- Convex Optimization by Stephen Boyd and Lieven Vandenberghe, Cambridge University Press, 2004. (Available online)
- Information Theory, Inference, and Learning Algorithms by David Mackay, Cambridge University Press, 2003. (Available online)
- Deep Learning by Yoshua Bengio, Ian Goodfellow, and Aaron Courville. (Available online)
Some useful articles
Software
- Python : we'll be using python throughout the course to implement various ML algorithms and run experiments
- Google Developer Python Tutorial (highly recommended as a way to master python in just a few hours!)
- NumPy Tutorial (also highly recommended!)
- Python tutorial (work at least through section 5; skip sections 2, 3.1.3)
- Python quick reference
- scikit-learn machine learning in Python
- tensorflow open-source low-level machine learning library
- keras Python deep learning library
- weka be sure to use the "Stable Book 3rd Edition" version. Weka is built using java, so you can download it into your home directory and run it directly there.
- Latex