Department of Computer Science and Engineering

- Homework 6 is due at
**2pm**on December 1 (Saturday), 2018. - Homework 5 is due on November 18 (Sunday), 2018.
- Homework 4 is due on November 4 (Sunday), 2018.
- Homework 3 is due on October 23 (Tuesday), 2018.
- Homework 2 is
~~due on September 28 (Friday), 2018.~~The deadline is extended to**2pm**, September 30 (Sunday), 2018. - Homework 1 is due on September 12 (Wednesday), 2018.
- There will be two evening exams. We will announce the locations during the semester.
- The Matrix Cookbook is a great resource.
- Here's a page with some useful introductory Matlab resources.
- We will use Gradescope for submitting homework assignments. Use the entry code M6Z8XD to access the class.
- We will use Piazza for discussions and questions. This will serve as a permanent announcement linking to the Piazza page. You can sign up for the class on Piazza here.
- Welcome to CSE 417T: Introduction to Machine Learning!

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 2:30-4:00PM | Fri 10:00-11:00AM Jolley 224 |

2 | Chien-Ju Ho | chienju.ho at wustl dot edu | Mon/Wed 2:30-4:00PM | Thu 2:00-3:00PM Jolley 510 |

3 | Bradley Flynn | bflynn at wustl dot edu | Tue 6:00-9:00PM |

**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 6:00-8:00PM Jolley 224 |

Adam Kern | adam.kern at wustl dot edu | Mon 11:00AM-1:00PM Jolley 431 |

Chunyuan Li | chunyuan at wustl dot edu | Thu 10:00AM-12:00PM Jolley 224 |

Daniel Teich | dteich at wustl dot edu | Tue 12:00-2:00PM Jolley 224 |

Jack Grundy | jmgrundy at wustl dot edu | Thu 6:00-8:00PM Jolley 224 |

Ryun Han | r.han at wustl dot edu | Wed 1:00-3:00PM Jolley 224 |

Trevor Larsen | trevorlarsen at wustl dot edu | Fri 11:00AM-1:00PM Jolley 517 |

Zachary Mekus | zmekus at wustl dot edu | Wed 10:00AM-12:00PM Jolley 431 |

**Office Hours**

We will announce the office hours during the first two weeks of the semester.

- AML (or LFD):
*Learning From Data*, Abu-Mostafa, Magdon-Ismail, and Lin.

- CASI:
*Computer Age Statistical Inference*, Efron and Hastie (PDF available on the textbook website.)

Date | Topics | Readings/Slides | Assignments |

Aug 27/28 | Introduction. Course policies. Course overview. Perceptron learning algorithm. | AML 1.1, 1.2; Lecture Slides (Chai) (Annotated). Lecture Slides (Ho). | |

Aug 29/30 | Generalizing outside the training set, Hoeffding's inequality, and multiple hypotheses. | AML 1.3; Lecture Slides (Annotated) | hw1 Submission instructions. |

Sep 4/5 | Matlab Tutorial, error and noise | AML 1.4; Lecture Slides (Annotated) | |

Sep 6/10 | Infinite hypothesis spaces, growth functions | AML 2.1.1; Lecture Slides (Annotated) | |

Sep 11/12 | VC-dimension | AML 2.1.1-2.1.3; Lecture Slides (Annotated) | |

Sep 13/17 | VC-dimension, bias-variance tradeoff | AML Rest of Chapter 2; Lecture Slides (Annotated) | hw2 |

Sep 18/19 | The pocket algorithm, linear regression. | AML 3.1-3.2; Lecture Slides (Annotated) | |

Sep 20/24 | Logistic regression, gradient descent. | AML 3.3; Lecture Slides (Annotated) | |

Sep 25/26 | (Stochastic) Gradient descent, nonlinear transformations. | AML 3.3-3.4; Lecture Slides (Annotated) | |

Sep 27/Oct 1 | Overfitting. | AML 4.1; Lecture Slides (Annotated), Malik Magdon-Ismail's slides on overfitting | |

Oct 2/3 | Review | Lecture Slides | hw3 |

Oct 8/9 | Regularization | AML 4.2; Lecture Slides (Annotated) | |

Oct 10/11 | Validation | AML 4.3; Lecture Slides (Annotated) | |

Oct 17/18 | Three Learning Principles | AML 5; Lecture Slides (Annotated) | |

Oct 22/23 | Decision Trees and ID3 | Tom Mitchell, Machine Learning Ch3; CASI 8.4; Lecture Slides (Annotated) | hw4 |

Oct 24/25 | Bagging. Random Forest. | CASI 17.1; Lecture Slides (Annotated) | |

Oct 29/30 | Boosting. AdaBoost. | Freund & Schapire's Tutorial. CASI 17.4; Lecture Slides (Annotated) | hw5 |

Oct 31/Nov 1 | Nearest Neighbor. | AML eChapter 6.1-6.2.1; Lecture Slides (Annotated) | |

Nov 5/6 | Efficiency of kNN. | AML eChapter 6.2-6.3; Lecture Slides (Annotated) | |

Nov 7/8 | Radial Basis Functions (RBFs). Support Vector Machines (SVMs) | AML eChapter 8.1; Lecture Slides (Annotated) | |

Nov 12/13 | Dual SVMs | AML eChapter 8.2, 8.4; Lecture Slides (Annotated) | |

Nov 14/15 | The Kernel Trick | AML eChapter 8.3; Lecture Slides (Annotated) | hw6 |

Nov 20 | Gaussian Processes (Optional) | GPML Chapter 2; Lecture Slides | |

Nov 19/27 | Neural Networks | AML eChapter 7.1, 7.2.1; Lecture Slides (Annotated) | |

Nov 28/29 | Backpropagation | AML 7.2.2, 7.2.3; Lecture Slides (Annotated) | |

Dec 3/4 | Final Review | Final Review |