“Equations are just the boring part of mathematics. I attempt to see things in terms of geometry.”
 Stephen Hawking (19422019)
Coordinates  [Syllabus] [Canvas] [Registrar] 
Instructor  Liang Huang (liang.huang@...) 
TA & Office Hours  Liang Zhang (zhanglia@...). Office Hours: M/W 78pm and F 56pm webex link 
Prerequisites 

Textbooks 

Grading 

Organization 
This course is organized into 5 Units,
each with 2 weeks and 1 programming HW which is usually out on Mondays and due on Saturdays the week after. See syllabus for more details. 
Communication 

Week  Topics (CIML References)  Slides/Handouts  Videos  homework/exercises/quizzes 

Unit 1: ML intro, kNN, and math/numpy review  
1  12. Introduction (0, 2.7)
3. Training, Test, and Generalization Errors (2.5, 2.6) Underfitting and overfitting (2.4) Leaveoneout crossvalidation (5.6). 4. knearest neighbor classifier (kNN) (3) 5. viewing HW1 data on terminal 67. data preprocessing: binarization  slides (15)
notebook (67) 
background survey (required) Quiz 1 (ML basics) HW1 out (kNN) [data]  
2 
Geometric Review of Linear Algebra Numpy Tutorial (also matplotlib): 1. ipython notebook; ndarray; %pylab; +//*, dot, concat 2. linear regression; np.polyfit; np.random.rand(); broadcasting 3. visualizing vectors operations; dot product, projection 
handout
slides
notebook 
Quiz 2 (numpy/linear algebra)
HW1 due  
Unit 2: Linear Classification and Perceptron Algorithm  
3  Linear Classification and Perceptron
1. Historical Overview; Bioinspired Learning (4.1) 2. Linear Classifier (4.3, 4.4); Augmented space (not in CIML) 3. Perceptron Algorithm (4.2) 4. Convergence Proof (4.5) 5. Limitations and NonLinear Feature Map (4.7, 5.4) 6. ipynb demo  slides
notebook (from [181])  EX out and due [tex] HW2 out [tex] (same data as HW1)  
4  Perceptron Extensions; Perceptron in Practice 1. Python demo 2. Perceptron Extensions: voted and averaged (4.6) 3. MIRA and aggressive MIRA (not in CIML) 4. Practical Issues (5.1, 5.2. 5.3, 5.4) 5. Perceptron vs. Logistic Regression (9.6)  slides demo  HW2 due  
Unit 3: Linear and Polynonmial Regression  
56  linear and polynomial regression (not in CIML)  HW3 (housing price prediction) kaggle data tex  
Unit 4: Applications: Text Classification  
78  Application: Text Classification Sentiment Analysis (thumbs up?)  HW4 [tex] data and code  
Unit 5: Exposure to cuttingedge ML research  
910  Paper Review: cuttingedge ML topics and papers  HW5 