(Fall 2022) CS688: Machine Learning

Instructor: Mingrui Liu

Description

Time/Location

• Wednesday 7:20pm—10:00pm, East 201

Office Hours:

• Monday 2pm—3pm or by appointment

Contact Information:

• Office: Research Hall 355

• Email: (firstname)l (at) gmu (dot) edu

Recommended Textbooks:

• Daumé. A Course in Machine Learning

• Bishop. Pattern Recognition and Machine Learning

• Hastie, Tibshirani, and Friedman. The Elements of Statistical Learning

• Goodfellow, Bengio, Courville. Deep Learning

Tentative Schedule

• Aug 24 (week 1): Introduction, Course Overview, Review Math/Python

• Aug 31 (week 2): Classfication, Regression

• Sep 7 (week 3): Bias-variance tradeoff, Model Selection

• Sep 14 (week 4): Probabilistic Generative Models, Probabilistic Discriminative Models

• Sep 21 (week 5): Convex Optimization

• Sep 28 (week 6): Support Vector Machines, Regularization, Margin

• Oct 5 (week 7): Kernel Methods, Duality

• Oct 12 (week 8): Midterm

• Oct 19 (week 9): Neural Networks, Backpropagation

• Oct 26 (week 10): Unsupervised Learning (Clustering, EM, Mixture Modeling)

• Nov 2 (week 11): Ensemble Methods

• Nov 9 (week 12): Reinforcement Learning

• Nov 16 (week 13): Final Project Presentation

• Nov 23: Thanksgiving Recess (No Class)

• Nov 30 (week 14): Final Project Presentation

Grading Policy

Grades:

• A: greater than 93

• A-: [90, 93)

• B+: [87, 90)

• B: [83, 87)

• B-: [80, 83)

• C+: [77, 80)

• C: [73, 77)

• C-: [68, 73)

• D: [60, 68)

• Fail: below 60

Weights:

• Homework: 50% (5 assignments in total)

• MidTerm: 20%

• Project proposal/presentation: 10%

• Project Report: 15%

• Class Participation: 5%

Late penalty:

• Late submissions for whatever reason will be punished. 5% of the score of an assignment/project will be deducted per day with maximum tolerance 3 days. For example, if an assignment is submitted 2 days and 1 minute later than the deadline (counted as 3 days) and it gets a grade of 90%, then the score after the deduction will be: 95% - 3*5% = 80%.

Honor Code