The mock midterm and the corresponding solutions are available.

The course summary from last year's iteration of the class was released. It is posted as-in, and may contain a mismatches with this year's iteration.

Stats for Project I grades have been posted.

Some exercise questions that will help you understand the text better. Remember, these DO NOT represent the questions that you will see in the exam!

PCML 2014 course summary (not updated) | ||

Lab: Gaussian Processes | Sheet | Code and Data |

Random Forests | Notes | |

Decision Trees | Notes | |

Gaussian Processes | Notes | Annotated |

Q&A for Project II, feedback on Project I | ||

Multi-Layer Perceptron | Notes | Annotated |

BayesNet and Belief Propagation | Notes | Annotated |

Lab: SVM | Sheet | Code and Data |

Examples of Time-series | Notes | |

SVD and PCA | Notes | Annotated |

Lab: Recommendation systems | Sheet | Code and Data |

Matrix factorization | Notes | Annotated |

Mock exam | Sheet | Solutions |

EM algorithm | Notes | Annotated |

Gaussian Mixture Model | Notes | Annotated |

Lab: K-means | Sheet | Code |

K-means | Notes | Annotated |

Unsupervised Learning | Notes | A note by P. Dayan |

Lab: Q&A on Project-I | ||

Support Vector Machines | Notes | Annotated |

Kernel Ridge Regression | Notes | Annotated, A note by M. Seeger |

Lab: Q&A on Project-I | ||

Curse of dimensionality and kNN | Notes | Annotated |

Generalized Linear Model | Notes | Annotated |

Lab: Logistic regression | Sheet | |

Logistic regression | Notes | Annotated |

Classification | Notes | Annotated |

Lab: Cross validation & bias-variance decomposition | Sheet | cvDemo.m |

Bias-Variance decomposition | Notes | Annotated |

Cross-Validation | Notes | Annotated |

Lab: Testing Linear Regression | Sheet | Code & data |

Ridge Regression | Notes | Annotated |

Overfitting | Notes | Annotated |

Maximum Likelihood | Notes | Annotated |

Least Squares | Notes | Annotated, PDF on ill-conditioning |

Lab: Linear Regression & Gradient Descent | Sheet | gradientDescent.m, gridSearch.m |

Gradient Descent | Notes | Annotations |

Cost Functions | Notes | Annotations |

Lab: Introduction to Matlab | Sheet | Data |

Linear Regression | Notes | Annotations |

Regression | Notes | Annotations |

Linear Algebra | Handout | Handout 2 |

Course Information | Notes | |

Introduction to PCML | Slides | Teacher's Slides |

Evaluation is based on the following methods.

- 10% Project-I
- 30% Project-II
- 60% Exams

**Define**the following basic machine learning problems and**explain**main differences between them: Regression, classification, clustering, dimensionality reduction, time-series.**Describe**a few important models and algorithms for the basic ML problems.**Implement, apply, and compare**these methods to real-world problems.**Choose**a method for the real-world problem in hand.**Critique and defend**your choice of method.**Derive**the theory behind ML methods taught in the course and**generalize**them to new problems.

- Becker Carlos Joaquin
- Bermudez Chacon Roger
- Newton Taylor Howard
- Salehi Farnood
- Seguin Benoit Laurent Auguste
- Victor Kristof
- Dennis Meier
- Merlin Nimier-David
- Jakub Sygnowski (voluntary TA)
- Michalina Pacholska (voluntary TA)