Slides Submission: Please submit the following file containing your slides via myWPI (submission name: MetaLearningGroupSlides) by 1 pm on Tuesday, Dec. 15th, 2015:
[your-lastnames]_proj2_slides.[ext]This file should be either a PDF file (ext=pdf) or a PowerPoint file (ext=pptx). Please use only lower case letters in the filename. List the lastnames in alphabetical order separated by "_". For instance, the file with my slides with teammates Bayes and Gauss would be named bayes_gauss_ruiz_proj2_slides.pptx
Phase I individual reports: 30% of Project 2 grade. Phase I individual test (Dec. 11th): 70% of Project 2 grade. Phase II presentation: 1.5% of course grade. Class participation during presentations: additional points.
Bonus points:Students who contribute informative entries to the CS539inR Wiki will receive some bonus points. Let me know when you contribute to the Wiki.
Dataset: For this part of the project, you will use the OptDigit Dataset available at the UCI Machine Learning Repository.
- Carefully read the description provided for this dataset and familiarize yourself with the dataset as much as possible.
- Use the following files:
- optdigits.names
- optdigits.tra: training dataset
- Remove the class attribute so that it is not used when clustering is performed.
Dataset: For this part of the project, you will use the OptDigit Dataset available at the UCI Machine Learning Repository.
- Carefully read the description provided for this dataset and familiarize yourself with the dataset as much as possible.
- Use the following files:
- optdigits.names
- optdigits.tra: training dataset
- optdigits.tes: test dataset
Dataset: For this part of the project, you will use the
Statlog (Heart) Dataset available at the
UCI Machine Learning Repository.
Carefully read the description provided for this dataset and
familiarize yourself with the dataset as much as possible.
Dataset: For this part of the project, you will use the OptDigit Dataset available at the UCI Machine Learning Repository.
- Carefully read the description provided for this dataset and familiarize yourself with the dataset as much as possible.
- Use the following files:
- optdigits.names
- optdigits.tra: training dataset
- optdigits.tes: test dataset
Topology of your Neural Net:
Dataset: For this part of the project, you will use the OptDigit Dataset available at the UCI Machine Learning Repository.
- Carefully read the description provided for this dataset and familiarize yourself with the dataset as much as possible.
- Use the following files:
- optdigits.names
- optdigits.tra: training dataset
- optdigits.tes: test dataset
Dataset: For this part of the project, you will use the Congressional Voting Records Dataset available at the UCI Machine Learning Repository.
For this part, it would be useful to look at my Matlab Naive Bayes example: diabetes_no_attribute_names.dat and naive_bayes_example_diabetis.m.
For this part, it would be useful to look at my Matlab examples: hmmgenerate_fair_loaded_coins_HMMs_tutorial_example.m and mmgenerate_pepsi_coke_HMMs_tutorial_example.m
PPPPCCPPPCCCPCCCCCPPPCPCPWhat is the probability that this sequence was generated by our HMM? Explain.
The topic for Phase II is combining multiple models, also called meta-learning. Investigate this topic using the following resources: