###IAP 2017 course at MIT
This course gives a high-level overview of diverse areas of machine learning. The goal is to introduce students to core concepts and techniques in ML, and provide enough of a primer on different sub-areas of ML so that students can choose the right approach for a given problem and explore interesting topics further.
The course covers an introduction to ML, Inference, Bayesian Methods and Neural Networks. Each class is taught by graduate students or post-docs at MIT working in the specific areas.
Organized by Manasi Vartak and Maggie Makar from MIT CSAIL.
This session gives an overview of supervised and unsupervised learning, and an introduction to probabilistic graphical models.
Concepts: Loss functions, Linear regression, Logistic regression, SVMs, Decision trees, Random Forests, Clustering, PCA, Graphical Models, Variable Elimination
Taught by Manasi Vartak.
- MIT 6.867 Machine Learning
- Coursera Machine Learning
- MIT 9.520 Statistical Learning Theory
- CMU: Intro to Machine Learning
- Michael Jordan Review of Graphical Models
- Coursera Probabilistic Graphical Models
- Columbia University: Probabilistic Graphical Models
This session gives an overview of (approximate) inference for probabilistic graphical models.
Concepts: Gaussian Mixture Models, Variational Inference, Monte Carlo Sampling
Taught by Maggie Makar ###Slides
- Tutorial on VI
- A Review of recent work on VI, Section 5
- Tutorial on Sampling methods
- A review (and really cool demos) of recent work on sampling
This session gives a whirlwind tour of Bayesian Methods in ML.
Concepts: What does it mean to be Bayesian in ML, Why be Bayesian, Posterior Inference, Parameteric vs. Non-Parametric Bayes
Taught by Trevor Campbell ###Slides
See slides!
This session gives an overview of neural networks, particularly as applied to computer vision.
Concepts: Neural Nets, Convolutional NNs, AlexNet, GoogleLeNet, Transfer learning
Taught by Carl Vondrick ###Slides Note: these slides are not exactly the ones that were presented in class. Please feel free to reach out to Carl if you need information that's not in these slides.