Note: this syllabus is tentative and subject to change. Book readings refer to the books listed below, unless otherwise noted. Some readings are available from the library's electronic reserves, for which you will need a userID and password that were distributed via email. (Contact the course staff if you need them to be resent to you.) You also can get these from blackboard using your OIT userID and password.
(RS)Tu, 2/6  Introduction  scribe notes  
Th, 2/8  Probability and statistics review  scribe notes ; slides  
 
Tu, 2/13  Introduction to classification and the Knearestneighbor algorithm  scribe
notes Mitchell, pp.230236 

Th, 2/15  High dimensional space  scribe notes Sections 9.59.6 of Coding and Information Theory (2nd ed, 1986) by R.W. Hamming 

Decision trees  Mitchell, Chapter 3  
Tu, 2/20  Computational learning theory  scribe notes  
Th, 2/22  Boosting  scribe notes  
Tu, 2/27  Support vector machines 
scribe notes Sections 5.45.7 of The nature of statistical learning theory (1995) by V.N. Vapnik  Burges's tutorial on svm's 


Th, 3/1  Kmeans clustering  scribe notes;
slides
Hastie et al., Sections 14.114.3 (on ereserve or blackboard) 

Tu, 3/6  Agglomerative clustering  scribe notes ; slides  


Th, 3/8  Introduction to graphical models  scribe notes  Jordan's Statistical Science paper 
Tu, 3/13  Naive Bayes classification  case study slides ; scribe notes  
Th, 3/15  Mixture models, latent variable models and EM  scribe notes; "Introduction to Graphical Models" 10.1, 11 (handout outside CS204) 

Tu, 3/27  Expectationmaximization  scribe notes  
 
Th, 3/29 Tu, 4/3  Introduction to regression; Linear regression  scribe notes 3/29 scribe notes 4/3 Hastie et al., pp. 4145, 5565, 115120 (on ereserve or blackboard); Bishop, pp. 137152, except sections 3.1.2 and 3.1.5 (there is some overlap between these readings that you can feel free to skim) 

Th, 4/5  Logistic regression 
scribe notes Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression (new, unpublished chapter of the Mitchell book) 

 
Tu, 4/10 Th, 4/12  Principal components analysis 
scribe notes 4/10; scribe notes 4/12; Hastie et al., sections 14.514.6 (on ereserve or blackboard) 

Th, 4/19  Factor analysis  scribe notes 4/19 ; MV Gaussian examples; Jordan Ch. 1314 (outside of Dave's door)  
 
Tu, 4/17  Maximum entropy modeling (RS) 
scribe notes "A maximum entropy approach to species distribution modeling"  slides; paper on modeling "bakeoff" 
Tu, 4/24  Applications to computational biology (guest lecture by Prof. Olga Troyanskaya)  scribe notes (draft); slides  
Th, 4/26  Applications to computer vision (guest lecture by Prof. FeiFei Li)  scribe notes ; slides  
Tu, 5/1  Computational neuroscience and fMRI data (guest lecture by Prof. Ken Norman)  scribe
notes ; slides; "Beyond mindreading: multivoxel pattern analysis of fMRI data"  
Th, 5/3  Topic models: Hidden variable models for large document collections (and class summary) (DB)  scribe notes ; slides 