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Description
CIS 5200 provides a fundamental introduction to the mathematics, algorithms and practice of machine learning, focusing on representation, loss functions, and optimization. Topics covered include:
- Supervised learning: least squares regression, logistic regression, L0/L1/L2 feature selection/regularization, online learning, boosting, Naive Bayes, support vector machines, ensemble methods, neural nets/deep learning
- Unsupervised learning: PCA, K-means clustering, Gaussian Mixture Models, EM, HMMs, Bayesian networks
- Reinforcement learning: TD-learning, Q-learning, deep learning
Audience
The course is aimed broadly at advanced undergraduates and beginning graduate students in computer science, electrical engineering, mathematics, physics, and statistics. This is a hard course; A good alternative for those with less math background or time is CIS419/519 or, if you want a really nice, much easier intro, take the Coursera ML course. If unsure which to take, see this.
Software
We will be coding in Python, using the Jupyter/SKLearn/Pytorch libraries, running on Google Colab.
Pre-requisites
- Basic probability and statistics (random variables, covariance matrix, CDF/PDF, Gaussian and other distributions, multiple regression). [CSE 261]
- Basic linear algebra (matrices, vectors, rank, basis, projection, inverse, eigenvectors).
- Reasonable python programming skills, including basic knowledge of python.
Reading Materials
- For the mathematical side of ML: C. Bishop, Pattern Recognition and Machine Learning. 2007
- For classical ML in Scikit-learn: hands on machine learning
- For deep learning in pytorch: Dive into Deep Learning
- example final projects demo1 and demo2
- See also Resources and Lectures