You are required to work on the Heritage Health Competition on the Kaggle website or on the "Give Me Some Credit" Competition on the Kaggle website
Please subscribe to one of these competitons and download the data.
For the Heritage Competition we have provided you with a simple set of features to get you started (thanks to Sungjin Ahn!), but you should feel free to change these features into anything else. The login and password to download these features will be provided in class.
You may form a team with up to 3 people. Use the following naming convention for your team: UCI-CS273A-WelShaEin where "WelShaEin" are the first 3 letters of my last name (Welling) and my collaborators Shannon and Einstein. You are required to submit your results regularly to the website and appear on the leaderboard. If you end up high on the leaderboard this will earn you extra bonus points (e.g. the team that wins the competition and receives the $3M will receive an A+ etc.)
By December 4 (midnight) you need to submit your final reports (see above). Don't make them too long (I can't read 20 reports of each 10 pages). However, you do need to report what you did and provide the appropriate analysis and interpretation of your best scoring method. Also make sure you make it abundantly clear where you got your information and who did what in your team.
(the following text was plagiarized from D.Kay)
Don't do it: Plagiarism means presenting somebody else's work as if it's your own. You may use whatever outside sources (books, friends, interviews, periodicals) are appropriate for an assignment, so long as you cite them: Any time you use two or more words in a row that you didn't think up and write yourself, you must put the words in quotation marks and indicate where they came from. (There could be situations where this two-word rule isn't appropriate. If you think you have one, check with us.) Even if you paraphrase (state in your own words) someone else's work or ideas, you should cite the source (e.g., "Dijkstra says that unrestricted branching is dangerous."). Plagiarism is academically dishonest, and we expect that nobody in the class will engage in it.
Turning in another person's work as your own violates the honesty policies of ICS and UCI (http://www.ics.uci.edu/ugrad/current/policies/index.php). The School of ICS takes academic honesty very seriously and imposes serious penalties on students who violate its guidelines. Detected violations could result in your failing the course, having a letter filed with the school, and losing a variety of other benefits and privileges. We do check for academic dishonesty both manually and automatically. It is an unfortunate fact that nearly every quarter, some students in ICS classes are found to have violated these policies; to protect the privacy of the guilty, violations are not made public, but sadly, they do occur. Compared to the consequences of academic dishonesty, one low assignment score is a minor disadvantage. If you feel as if you're falling behind or have other difficulties, see the instructor; we will help you work around your trouble. No matter how pressured you feel, don't plagiarize; it's not worth it.
Most importantly, realize that getting "the answer" is only the last part of each assignment. Equally important is the process of getting the solution—including the false starts, bugs, misconceptions, and mistakes—because the learning occurs in the doing. Completely apart from the ethical issues, copying a solution deprives you of the whole point of the assignment.
-Introduction, Scatter Plots, kNN, Logistic Regression, Xvalidation, overfitting [ppt] [pdf]
-Decision Theory, Loss functions, ROC curves [ppt][pdf]
-Clustering [ppt] [pdf]
-PCA [ppt] [pdf]
-Convex Optimization (see classnotes and book)
-SVMs [ppt] [pdf]
-Kernel-PCA (see classnotes and book)
-Spectral Clustering (classnotes)
-FisherDiscriminant Analysis (classnotes)
-Ridge Regression & Kernel RG (classnotes)
-Neural Networks [ppt] [pdf]
-Bias-Variance Tradeoff [ppt] [pdf]
-DT [ppt] [pdf]
-Boosting [ppt] [pdf]
For your own interest:
-Classifier Evaluation [ppt][pdf]
-Canonical Correlation Analysis & Maximum Autocorrelation Factor Analysis (classnotes)
Practice Exams
Week 1: Homework 1 [doc] [pdf]
Week 2: Homework 2 [doc] [pdf]
Week 3: Homework 3 [doc] [pdf]
Week 4: Homework 4 [doc] [pdf]
Week 5: Homework 5 [doc] [pdf]
Week 6: Homework 6 [doc][pdf]
Week 7: Homework 7 [doc] [pdf]
Week 8: Homework 8 [doc][pdf]
Week 9: Homework 9 [doc] [pdf]
For your own interest:
Week 10: Homework 10 [doc][pdf]