# Homework Assignments

# | Out | Due | Description | Problems | Materials |
---|---|---|---|---|---|

1 | 2/03 | 2/10 | Naive Bayes Classification | questions template |
nursery data |

2 | 2/10 | 2/17 | ML & Bayesian Estimation | questions | iris species |

3 | 2/17 | 2/25 | Handwritten Digit Classification | questions | mnist digits |

4 | 2/25 | 3/04 | Linear Regression | questions | motorcycle data |

5 | 3/03 | 3/11 | Logistic Regression | questions | nursery data toy data: a, b, c |

6 | 3/18 | 3/25 | Regularization & Sparsity | questions | dorothea data |

7 | 4/08 | 4/19 | Gaussian Processes & K-Means Clustering |
questions | pedestrian data: raw,
hog mnist digits |

8 | 4/20 | 4/29 | Principal Components Analysis EM Algorithm |
questions | motorcycle data face data |

9 | 4/29 | 5/10 | Hidden Markov Models Topic Models & MCMC |
questions | Alice train,
test bars data, NIPS data |

Homework assignments combine mathematical derivations with programming exercises in Matlab. If you have questions, come to our office hours or e-mail cs195fheadtas-at-cs. To hand-in your solutions, which are due at noon *before* lecture, use the following procedure:

- Solve
- Type your answers in LaTeX, numbered by question and part as in the assignment. Be sure to include your full name and your CS account username at the top of the first page. Compile your document into a pdf. LaTeX editing can be easiest with specialized editors like Texmaker or Kile (available on department workstations).
*All*of your answers should be in a single pdf: include result plots via the LaTeX figure environment, and Matlab source code in clearly labeled sections at the*end*of the pdf via the verbatim environment. Please be clear, and make it easy for the graders to check your work! - Review
- Change to the directory (
**cd**) your work is in. When you list files (**ls**), the only file should be**hw.pdf**. Matlab source code should be included at the end of the pdf, as described above. The Matlab code doesn't need to be extensively documented, but it should be readably commented, and we may run it. You should*not*turn in any folders. - Submit
- Execute
**/course/cs195f/bin/cs195f_handin hw?**, replacing**?**by the appropriate homework number. This has been tested to work, but if it doesn't for any reason, e-mail your solutions to mbarrows-at-cs with a full description of the problem, including any warning messages (and/or visit office hours).

# Midterm and Final Exams

The midterm exam will be given on Tuesday, March 15 at the normal lecture time (1:00-2:20pm in CIT 368). The final exam will be given on Thursday, May 19 from 2:00-4:50pm in MacMillan 115.

These will be pencil and paper exams, with answers written on blue books distributed at the start of the exam. Computers and calculators will not be necessary, nor are you allowed to use them. A formula sheet will be distributed with the exam, similar to this example from last year.

# Graduate Credit Projects

Masters and doctoral students in the Brown computer science department can receive *2000-level graduate credit* by completing an additional course project. This credit fulfills internal computer science degree requirements, but it is not recorded by Brown's registrar, and is probably not useful to students majoring in other fields.

For the project, you should apply material from (or closely related to) the course to a problem or dataset that you care about. Your experiments and analysis need not be sufficient for publication in a major conference, but should go beyond the typical homework problem. You should try to study combinations of statistical models, learning algorithms, datasets, and/or features which have not been previously explored.

A poor or incomplete project won't hurt your grade, but will mean you don't receive graduate credit. To successfully complete a project, you must fulfill the following requirements.

- Proposal
- Prepare a short, 2-3 paragraph proposal describing the problem you will study, the models and learning algorithms you will experiment with, and why you think this application is promising. Specify a specific dataset (which you have access to) to be used in your experiments. Also include at least two references relevant to your project (either sections of books or research articles). Proposals should be submitted by e-mail to the instructor by
**11:59pm on Thursday, April 7**. - Presentation
- Prepare a short oral presentation summarizing the results of your project. Presentations will be given during the normal lecture time (1:00-2:20pm in CIT 368) on
**Thursday, May 12**. - Report
- Prepare a 4-8 page technical report describing the results of your project, in the LaTeX style of your homework solutions. Your report should analyze the quantitative performance of the tested methods, using good practices taught in class (e.g., use of separate validation and test sets). You should also explain any interesting qualitative features of your experimental results, both good and bad. Reports should be submitted by e-mail to the instructor by
**11:59pm on Tuesday, May 17**.