Date | Subject | Reading | Questions |

On your own | review probability | Probability Review Bishop 1.1-1.4, slides MIT Probability Open Course | | |

On your own | review linear algebra | Strang’s MIT OpenCourse MLMath | Self Test, Self Test Solutions, |

W/Aug 29 | Intro, (pdf) | | |

F/Aug 31 | Point Estimation: MLE and MAP probability densities (pdf) | supplemental:Bishop 2.1, Appendix B, covariance | quiz |

M/Sep 3 | No class: Labor Day | | |

W/Sep 5 | Local learning Norms (pdf) | Bishop 2.5 | quiz |

F/Sep 7 | Intro to Matlab Tutorial | Coursera octave tutorial | quiz |

M/Sep 10 | Decision Trees and information theory (pdf) | Decision Trees by N. Nilsson, Bishop 1.6 Bishop 14.4 KL and mutual info | quiz |

on your own | Gaussians | Bishop 1.2.4, Bishop 2.3.1-2.3.3 | |

W/Sep 12 | Regression, Regression slides | Bishop 1.1-1.4, Bishop 3.1, 3.1.1, 3.1.4, 3.1.5, 3.2, 3.3, 3.3.1, 3.3.2 | pre-quiz |

F/Sep 14 | RBF, gradient descent | | quiz |

M/Sep 17 | Overfitting and Regularization, Bias Variance Decomposition, Bias/Variance for Regression | Bishop 1.3, 1.5, 3.2 applet scale_invariance | quiz |

W/Sep 19 | regression penalty slides Stepwise, streamwise, stagewise | short video:stepwise regression,Hastie et al. 7.1-7.3 LASSO (supplemental reading) | quiz |

F/Sep 21 | MDL (pdf) | MDL-supplemental MDL-background | quiz |

M/Sep 24 | Classification Logistic Regression, Naive Bayes slides
| Bishop 4.0, 4.2- 4.5 | quiz |

W/Sep 26 | Neural Net slides Supervised Deep Networks | CNN description supplemental: deep net tutorial | quiz |

F/Sep 28 | more deep learning Supervised Deep Networks | supplemental: gay learning | |

M/Oct 1 | Optimization,(pdf),kernels (pdf) | Constrained Optimization | quiz |

W/Oct 3 | Support Vector Machines (pdf)
| Additional notes, Bishop 7.1 (Max Margin), Hearst 1998 | |

F/Oct 5 | No class: Fall Break | | |

M/Oct 8 | Perceptrons (pdf) | Online Learning and Perceptrons | quiz |

W/Oct 10 | Boosting gradient boosting | Boosting,Bishop 14.3, Schapire’s Tutorial
| |

F/Oct 12 | Midterm Review (pdf) | Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, Cross-Validation, Boosting Complexity consistency | |

M/Oct 15 | Midterm | Sample questions 2016,2017 and answers 2016,2017. | quiz |

W/Oct 17 | SVD SVD slides | Kosecka’s review slides | quiz |

F/Oct 19 | PCA,(pdf),eigenwords | Bishop 12.1: PCA Bishop Appendix C Properties of Matrices PCR, PLS and CCA | quiz |

M/Oct 22 | Unsupervised Deep Networks | | quiz |

W/Oct 24 | Unsupervised Learning: Clustering, K-means EM | Bishop 9.1-9.3 | quiz |

F/Oct 26 | LDA slides | Supplemental:LDA intro and original LDA paper | quiz |

M/Oct 29 | No class | | |

W/Oct 31 | Generative PCA, CCA | Bishop 12.1–12.3, supplemental:Neal and Hintons | quiz |

F/Nov 2 | Netflix,(pdf) | supplemental:netflix | |

M/Nov 5 | Project overview and advice and project slides; Real world ML loss functions | More advice, supplemental:Performance Measures | |

W/Nov 7 | Belief Nets (pdf) | Jordan Bayes Net chapter, additional notes, supplemental: Koller paper | quiz |

F/Nov 9 | HMMs pdf | Hidden Markov Models supplemental: Rabiner’s HMM Tutorial, Bishop 13.1–2 | quiz |

M/Nov 12 | Recurrent Nets (pdf) manifolds (pdf) | RNNs; supplemental:LSTMs | |

W/Nov 14 | Active Learning (pdf) | Semi-Supervised and Active Learning active learning survey (sections 3.1–3.5 are the key ones; everything after that is supplementatl) | |

F/Nov 16 | Experimental design (pdf), Missing data (pdf) | | |

M/Nov 19 | GANS, ML Overview | | |

W/Nov 21 | No class | | |

F/Nov 23 | No class — Happy Thanksgiving!! | | |

M/Nov 26 | Reinforcement Learning | supplemental: additional reinforcement learning exercise | |

W/Nov 28 | Reinforcement Learning | supplemental: additional reinforcement learning exercise | |

F/Nov 30 | Structured Prediction | supplemental: Structured Prediction | |

M/Dec 3 | Causality?, Variable Importance | | |

W/Dec 5 | Big Data | Unreasonable effectiveness of data | |

F/Dec 7 | The Future of ML and humanity | supplemental: job futures | |

M/Dec 10 | Final project awards; Other material current deep learning ML Overview
| LIONbook - a quick review; After CIS520 | |

T/Dec 11 | Review Session: 5:00–6:00 pm Building: Towne 100 | Final Review Review Questions | |

F/Dec 14 | Final: 9:00am-11:00am Chem 102 | 2016 final and solution | |