Lecture Schedule
Date | Topic | Materials |
Jan. 5 | Introduction. Linear models. | Lecture 1 slides Bishop, Sec. 1.1, 3.1, 3.2 (or equivalent) If you need to catch up on the math, a brief probability review and linear algebra and matrix calculus review from Stanford University; also, Bishop appendix B,C. |
Jan.10 | More on linear models. Overfitting. Regularization. | Lecture 2 slides Bishop, Sec. 3.1, 3.2 |
Jan.12 | More on Bayesian and maximum likelihood fitting. Logistic regression | Lecture 3 slides Bishop Sec. 3.3, 4.3 (or equivalent) |
Jan.17 | More on logistic regression. Introduction to kernels. | Lecture 4 slides Bishop, Sec. 6.1, 6.2 |
Jan.19 | More on kernels. Introduction to Support vector machines. | Lecture 5 slides Bishop, Sec. 7.1 |
Jan.24 | More on kernels. Supoort vector machines | Lecture 6 slides Bishop, Sec. 7.1 |
Jan.26 | Active learning |
Lecture 7 slides
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Jan.31 | Learning with structured data. Introduction to graphical models via mixture models. | Lecture 8 slides
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Feb. 2 | Representational power of directed graphical models. Inference methods | Same slide deck as last lecture |
Feb. 7 | Hidden Markov Models: Inference and Learning | Lecture 10 slides |
Feb. 9 | More on learning in directed graphical models | Lecture 11 slides |
Feb.14 | No class (watch Tibshirani invited talk at NIPS) | |
Feb.16 | Undirected graphical models | Lecture 12 slides |
Feb.21 | Dimensionality reduction: PCA, kernel PCA, LLE | Lecture 13 slides Bishop 12.1, 12.3 (or equivalent) Locally Linear Embeddings (optional) |
Feb.23 | Non-linear dimensionality reduction: auto-encoders | Lecture 15 slides Quoc Le tutorial on backpropagation and tutorial on autoencoders, convnets and recurrent nets (first part) |
Mar. 7 | Spectral methods for time series. | Lecture 16 slides (thanks to B. Balle, A. Quattoni and X. Carrreras) Machine Learning paper |
Mar. 9 | Learning dynamical systems: Bayesain updating, Kalman filters and friends | Lecture 17 slides |
Mar.14 | Recurrent Neural Networks | Lecture 18 slides Quoc Le tutorial on backpropagation and tutorial on autoencoders, conv nets and deep nets (last part) |
Mar.16 | Method of moments, latent variable models, and tensors | Lecture 19 slides JMLR paper |
Mar.21 | Recap for midterm |
Solutions to 2016 midterm> (disucssed in class)
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Mar.23 | More recap | Lecture 21 slides
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Mar.28 | In-class midterm exam |
Some examples of midteurm-style questions with solutions Midterm from 2016 |
Mar.30 | Generatice Adversarial Networks | Lecture 22 slides |
Apr. 4 | Computational learning theory | Lecture 23 slides
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Apr. 6 | Reinforcement learning for prediction | Lecture 24 slides |
Apr.11 | Reinforcement learning for control | Lecture 25 slides |