“Equations are just the boring part of mathematics. I attempt to see things in terms of geometry.”
 Stephen Hawking (19422021)
Coordinates  [Canvas] [Registrar] [Ed Discussion Forum] 
Instructor  Liang Huang (liang.huang@oregonstate.edu). 
TAs 
Zetian Wu (wuzet@oregonstate.edu) Milan Gautam (gautammi@oregonstate.edu) 
Office Hours  M, W, Th, F; exact slots TBD Zoom link (no passcode needed) 
Prerequisites 

Textbooks 

Grading 

Unit 1 (weeks 13): ML intro, \(k\)NN, and math/numpy review  

1.0  Introduction  
1.1  Machine Learning Settings  
1.2  Basic Machine Learning Concepts  
1.3  Nearest Neighbor Classifier  
1.4  Linear Algebra and Numpy Tutorials  
HW1  \(k\)NN for income classification [pdf] [data] [kaggle]  
Unit 2 (weeks 45): linear classification and perceptron  
2.1  History of Perceptron  
2.2  Linear Classification  
2.3  The Perceptron Algorithm  
2.4  Convergence Theorem and Proof  
2.5  Inseparable Cases and Feature Engineering  
2.6  Voted and Averaged Perceptrons  
HW2  perceptron for sentiment [pdf] [data] [kaggle]  
Unit 3 (weeks 67): linear and polynomial regression  
3.1  Linear Regression  
3.2  Regularize  
3.3  Gradient Descent  
3.4  Normal Equation  
3.5  Nonlinear Regression  
HW3  regression for housing price prediction [pdf] [data] [kaggle]  
Unit 4 (weeks 89): a taste of deep learning  
4.1  Multilayer Neural Networks  
4.2  Word Embeddings  
HW4  redo HW2 with word embeddings [pdf] (HW2 data + embeddings) [kaggle]  
Unit 5 (week 10): paper review (cuttingedge ML)  
HW5  paper review (see the list of papers on Canvas) 