Machine Learning
CMSC 422
Spring 2013
Machine Learning
CMSC 422 Spring 2013
|
15% | Written homeworks There are fifteen written homeworks (roughly one per week). Each is worth 1% of your final grade. They are graded on a high-pass (100%), low-pass (50%) or fail (0%) basis. These are to be completed individually. (The initial homework, HW00, is not graded, but required if you do not want to fail.) | |
30% | Programming projects There are three programming projects, each worth 10% of your final grade. You will be graded on both code correctness as well as your analysis of the results. These must be completed in teams of two or three students. | |
15% | Midterm exam There will be an in-class midterm exam, obviously to be completed individually. You may bring one sheet of notes. | |
30% | Final exam There will be a (cumulative) final exam, during the official slot, to be completed individually. You may bring one sheet of notes. | |
10% | Participation Both in-class and Piazza-based participation. You can get participation points by volunteering for stuff in class or answering or asking questions on Piazza. |
There are no official books for this course.Our primary source will
be a collection of notes (aka CIML) I
have been writing.
Other
recommended (but not required) books:
|
Date | Topics | Required Readings |
Optional Readings |
Due |
W 23 Jan | Welcome to machine learning | - | - | - |
F 25 Jan | Decision trees and inductive bias | CIML 1-1.6 | - | HW00 |
M 28 Jan | Dealing with data | CIML 1.7-1.10 | - | HW01 |
W 30 Jan | Lab: exploring trees, interpreting results | None | - | - |
F 01 Feb | Geometry and nearest neighbors | CIML 2-2.3 | - | - |
M 04 Feb | K-means clustering | CIML 2.4-2.6 | - | HW02 |
W 06 Feb | Lab: geometric models | None | - | - |
F 08 Feb | Perceptrons | CIML 3-3.5 | - | - |
M 11 Feb | Perceptrons II | CIML 3.5-3.7 | - | HW03 |
W 13 Feb | Lab: perceptrons and linear models | None | - | - |
F 15 Feb | The importance of good features | CIML 4-4.4 | - | - |
M 18 Feb | Catch-up | - | - | HW04 |
W 20 Feb | Evaluation and debugging | CIML 4.5-4.8 | - | P1 |
F 22 Feb | Lab: which algorithm is best? | None | - | - |
M 25 Feb | Imbalanced and multiclass classification | CIML 5-5.2 | - | HW05 |
W 27 Feb | Ranking and collective classification | CIML 5.3-5.5 | - | - |
F 01 Mar | Lab: multiclass classification | None | - | - |
M 04 Mar | Linear models and gradient descent | CIML 6-6.4 | - | HW06 |
W 06 Mar | Lab: gradient descent | None | - | - |
F 08 Mar | Class cancelled for visit day | - | - | - |
M 11 Mar | Subgradient descent and support vector machines | CIML 6.5-6.7 | - | HW07 |
W 13 Mar | Midterm review | None | - | - |
F 15 Mar | Midterm | None | - | - |
M 25 Mar | Naive Bayes models | CIML 7-7.5 | - | HW08 |
W 27 Mar | Lab: Naive Bayes | None | - | P2 |
F 29 Mar | Conditional probabilistic models | CIML 7.6-7.7 | - | - |
M 01 Apr | Neural networks | CIML 8-8.3 | - | HW09 |
W 03 Apr | Deep neural networks | CIML 8.4-8.6 | - | - |
F 05 Apr | Kernels | CIML 9-9.3 | - | - |
M 08 Apr | No class | - | - | HW10 |
W 10 Apr | Lab: kernels | None | - | - |
F 12 Apr | No class | - | - | - |
M 15 Apr | Support vector machines II | CIML 9.4-9.6 | - | HW11 |
W 17 Apr | K-means revisited | CIML 13-13.1 | - | - |
F 19 Apr | PCA and kPCA | CIML 13.2 | - | - |
M 22 Apr | Digging into Data I | Unix4Poets | - | HW12 |
W 24 Apr | Lab: digging into data | None | - | - |
F 26 Apr | Digging into Data II | - | - | - |
M 29 Apr | Online learning | TBD | - | HW13 |
W 01 May | Markov decision processes | Chapter 3 | - | - |
F 03 May | Imitation learning: DAgger | TBD | - | - |
M 06 May | Lab: imitation learning | None | - | P3,HW14 |
W 08 May | Review for final exam | None | - | - |
F 17 May | Final Exam, 1:30-3:30pm | - | - | - |