machine_learning_cmu_2015 (34 files)
Type: Course
Bibtex:
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Bibtex:
@article{,
title= {10-715 Advanced Introduction to Machine Learning - CMU - Fall 2015},
keywords= {},
journal= {},
author= {Barnabas Poczos and Alex Smola },
year= {},
url= {https://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/index.html},
license= {},
abstract= {The rapid improvement of sensory techniques and processor speed, and the availability of inexpensive massive digital storage, have led to a growing demand for systems that can automatically comprehend and mine massive and complex data from diverse sources. Machine Learning is becoming the primary mechanism by which information is extracted from Big Data, and a primary pillar that Artificial Intelligence is built upon.
This course is designed for Ph.D. students whose primary field of study is machine learning, or who intend to make machine learning methodological research a main focus of their thesis. It will give students a thorough grounding in the algorithms, mathematics, theories, and insights needed to do in-depth research and applications in machine learning. The topics of this course will in part parallel those covered in the general graduate machine learning course (10-701), but with a greater emphasis on depth in theory and algorithms. The course will also include additional advanced topics such as RKHS and representer theory, Bayesian nonparametrics, additional material on graphical models, manifolds and spectral graph theory, reinforcement learning and online learning, etc.
Students entering the class are expected to have a pre-existing strong working knowledge of algorithms, linear algebra, probability, and statistics. If you are interested in this topic, but do not have the required background or are not planning to work on a PhD thesis with machine learning as the main focus, you might consider the general graduate Machine Learning course (10-701) or the Masters-level Machine Learning course (10-601).
| Lecture | Block | Topic | Lecturer | | |
|---------|--------|----------------------|-------------------------------|--------------------------------------------------------------|----------|
| 1 | W | Sep 9 | Supervised Learning | Introduction to Machine Learning, MLE, MAP, Naive Bayes | Barnabas |
| 2 | M | Sep 14 | | Perceptron, Features, Stochastic Gradient Descent | Alex |
| 3 | W | Sep 16 | | Neural Networks: Backprop, Layers | Alex |
| 4 | M | Sep 21 | | Neural Networks: State, Memory, Representations | Alex |
| 5 | W | Sep 23 | Unsupervised Learning | Clustering, K-Means | Barnabas |
| 6 | M | Sep 28 | | Expectation Maximization, Mixture of Gaussians | Barnabas |
| 7 | W | Sep 30 | | Principal Component Analysis | Barnabas |
| 8 | M | Oct 5 | Kernel Machines | Convex Optimization, Duality, Linear and Quadratic Programs | Alex |
| 9 | W | Oct 7 | | Support Vector Classification, Regression, Novelty Detection | Alex |
| 10 | M | Oct 12 | | Features, Kernels, Hilbert Spaces | Alex |
| 11 | W | Oct 14 | | Gaussian Processes 1 | Barnabas |
| 12 | M | Oct 19 | | Gaussian Processes 2 | Barnabas |
| 13 | W | Oct 21 | Latent Space Models | Independent Component Analysis | Barnabas |
| 14 | M | Oct 26 | Graphical Models | Hidden Markov Models | Alex |
| 15 | W | Oct 28 | | Directed Models | Alex |
| 16 | M | Nov 2 | | Undirected Models | Alex |
| 17 | W | Nov 4 | | Sampling, Markov Chain Monte Carlo Methods | Alex |
| 18 | M | Nov 9 | Midterm exam | | |
| 19 | W | Nov 11 | Computational Learning theory | Risk Minimization | Barnabas |
| 20 | M | Nov 16 | | VC Dimension | Barnabas |
| 21 | W | Nov 18 | Nonlinear dim reduction | Manifold Learning | Barnabas |
| 22 | M | Nov 23 | Big data and Scalability | Systems for Machine Learning, Parameter server | Alex |
| W | Nov 25 | Thanksgiving Holiday | | | |
| 23 | M | Nov 30 | Project Presentations | | students |
| 24 | M | Dec 2 | Project Presentations | | students |},
superseded= {},
terms= {}
}
