Weak 1 | 1 | 3-Jan |
Introduction
(slides)
- Readings (Murphy): 1.1-1.4
| |

| 2 | 5-Jan |
Point estimation
(slides)
| |

Weak 2 | 3 | 10-Jan |
Linear regression
(slides)
- Readings (Murphy):
- Linear Regression Model: 7.1-7.3
- Error metrics, overfitting, bias-var tradeoff: 6.4
- Regularization, ridge regression: 7.5.1
- LASSO: 13.1, 13.3-13.4.1
- Python Tutorial
(link)
| |

| 4 | 12-Jan |
Classification, logistic regression
(slides)
- Readings (Murphy): 8.1 - 8.3, 8.5.2
| |

Weak 3 | 5 | 17-Jan |
Online learning: Perceptron
(slides)
- Readings (Murphy): 8.5.0, 8.5.4
| **Hw1** due |

| 6 | 19-Jan |
Margin-based approaches: SVM
(slides)
| |

Weak 4 | 7 | 24-Jan |
Structured models (Naive Bayes)
(slides)
| |

| 8 | 26-Jan |
Interesting applications
(slides)
| |

Weak 5 | 9 | 31-Jan |
Quiz 1
| Quiz1 |

| 10 | 2-Feb |
Neural Networks
(slides)
| Hw2 due |

Weak 6 | 11 | 7-Feb |
Deep Neural models
(slides)
| Project proposal due |

| 12 | 9-Feb |
Kernels
(slides)
| |

Weak 7 | 13 | 14-Feb |
Decision Trees
(slides)
| |

| 14 | 16-Feb |
Boosting and Ensemble methods
(slides)
| Hw3 due |

Weak 8 | 15 | 21-Feb |
Quiz2
| Quiz2 |

| 16 | 23-Feb |
HMMs and RNNs
(slides)
| |

Weak 9 | 17 | 28-Feb |
Project progress report presentation (3 minutes each)
(slides)
| |

| 18 | 2-Mar |
Clustering
(slides)
| HW4 due |

Weak 10 | 19 | 7-Mar |
EM
(slides)
| |

| 20 | 9-Mar | Poster presentation + Pizza | Final Project |