“Equations are just the boring part of mathematics. I attempt to see things in terms of geometry.”
-- Stephen Hawking (1942--2018)
Coordinates | [Registrar] [Canvas] [syllabus] | ||||||||||||
Instructor | Liang Huang (liang.huang@...) | ||||||||||||
TAs & Office Hours |
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Prerequisites |
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Textbooks |
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Grading |
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Email Policy | Please post all course-related questions on Canvas so that the whole class may benefit from our conversation. Please contact us privately only for matters of a personal nature (by default, please cc all TAs unless you want to complain about a TA). As a course policy we will not reply to any technical questions via email. |
Week | Topics and CIML References | Slides | Videos | homework | extra |
1 | Introduction (0, 2.7)
Training, Test, and Generalization Errors (2.5, 2.6) Underfitting and overfitting (2.4) Leave-one-out cross-validation (5.6) k-nearest neighbor classifier (k-NN) (3) | Quiz 1 (on Canvas) | |||
2 | Linear Classification and Perceptron
1. Historical Overview; Bio-inspired 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 Non-Linear Feature Map (4.7, 5.4) | EX1 | |||
3 | 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) | HW1 data | demo code | ||
4 | SVM (7.7) | EX2 | |||
5 | SVM (7.7); Kernels (11) | HW2 | |||
6 | Midterm | Midterm (on Canvas) | |||
7 | Structured Prediction (17) Hidden Markov Models (17) | EX3 | |||
8 | Structured Perceptron (17) Structured SVM (17) | HW3 | |||
9 | Unsupervised Learning Clustering: k-means (15), EM (16) Dimensionality reduction: PCA | Quiz 2 (on Canvas) | |||
10 | Deep Learning (10) Convolutional Neural Nets Word Embeddings Reccurent Neural Nets | Quiz 3 (on Canvas) |