Exam 1  10/4/2018 (Thu) 6:30 PM  8:30 PM  Hillman 60 (Odd student IDs); Wilson 214 (Even student IDs) 
Exam 2  12/5/2018 (Wed) 6:30 PM  8:30 PM  Lab Sciences 300 
Instructors:
Section  Instructor  Time  Office Hours  
1  Henry Chai  hchai at wustl dot edu  Tue/Thu 2:304:00PM  Fri 10:0011:00AM Jolley 224 
2  ChienJu Ho  chienju.ho at wustl dot edu  Mon/Wed 2:304:00PM  Thu 2:003:00PM Jolley 510 
3  Bradley Flynn  bflynn at wustl dot edu  Tue 6:009:00PM 
Graduate Assistants and TAs:
There are several graduate
assistants and undergraduate TAs for the class. All assistants will
hold regular office hours, answer questions on Piazza, and grade
homeworks. The graduate assistants will also hold occasional
recitation or review sessions.
TA  Office Hours  
Arghya Datta (Graduate Assistant to the Instructors)  arghya at wustl dot edu  Mon 6:008:00PM Jolley 224 
Adam Kern  adam.kern at wustl dot edu  Mon 11:00AM1:00PM Jolley 431 
Chunyuan Li  chunyuan at wustl dot edu  Thu 10:00AM12:00PM Jolley 224 
Daniel Teich  dteich at wustl dot edu  Tue 12:002:00PM Jolley 224 
Jack Grundy  jmgrundy at wustl dot edu  Thu 6:008:00PM Jolley 224 
Ryun Han  r.han at wustl dot edu  Wed 1:003:00PM Jolley 224 
Trevor Larsen  trevorlarsen at wustl dot edu  Fri 11:00AM1:00PM Jolley 517 
Zachary Mekus  zmekus at wustl dot edu  Wed 10:00AM12:00PM Jolley 431 
Office Hours
We will announce the office hours during the first two weeks of the semester.
Date  Instructor  Topics  Readings/Slides  Assignments 
Aug 27/28  Introduction. Course policies. Course overview. Perceptron learning algorithm.  AML 1.1, 1.2; Lecture Slides (Chai). Lecture Slides (Ho).  
Aug 29/30  Generalizing outside the training set, Hoeffding's inequality, and multiple hypotheses.  AML 1.3; Lecture Slides (Chai)  hw1 Submission instructions. 

Sep 4/5  Matlab Tutorial, error and noise  AML 1.4; Lecture Slides (Chai)  
Sep 6/10  Infinite hypothesis spaces, growth functions  AML 2.1.1; Lecture Slides (Chai)  
Sep 11/12  VCdimension  AML 2.1.12.1.3; Lecture Slides (Chai)  
Sep 13/17  VCdimension, biasvariance tradeoff  AML Rest of Chapter 2; Lecture Slides (Chai)  hw2  
Sep 18/19  The pocket algorithm, linear regression.  AML 3.13.2; Lecture Slides (Chai)  
Sep 20/24  Logistic regression, gradient descent.  AML 3.3; Lecture Slides (Chai)  
Sep 25/26  (Stochastic) Gradient descent, nonlinear transformations.  AML 3.33.4; Lecture Slides (Chai)  
Sep 27/Oct 1  Overfitting.  AML 4.1; Lecture Slides (Chai), Malik MagdonIsmail's slides on overfitting  
Oct 2/3  Review  Lecture Slides (Chai)  hw3  
Oct 8/9  Regularization  AML 4.2; Lecture Slides (Chai)  
Oct 10/11  Validation  AML 4.3; Lecture Slides (Chai)  
Oct 17/18  Three Learning Principles  AML 5; Lecture Slides (Chai)  
Oct 22/23  Decision Trees and ID3  Tom Mitchell, Machine Learning Ch3; CASI 8.4; Lecture Slides (Chai)  hw4  
Oct 24/25  Bagging. Random Forest.  CASI 17.1; Lecture Slides (Chai)  
Oct 29/30  Boosting. AdaBoost.  Freund & Schapire's Tutorial. CASI 17.4; Lecture Slides (Chai)  hw5  
Oct 31/Nov 1  Nearest Neighbor.  AML eChapter 6.16.2.1; Lecture Slides (Chai)  
Nov 5/6  Efficiency of kNN.  AML eChapter 6.26.3; Lecture Slides (Chai)  
Nov 7/8  Radial Basis Functions (RBFs). Support Vector Machines (SVMs)  AML eChapter 8.1; Lecture Slides (Chai)  
Nov 12/13  Dual SVMs  AML eChapter 8.2, 8.4; Lecture Slides (Chai)  
Nov 14/15  The Kernel Trick  AML eChapter 8.3; Lecture Slides (Chai)  hw6  
Nov 20  Gaussian Processes (Optional)  GPML Chapter 2; Lecture Slides (Chai) 