Number |
Date |
Topic |
Reading |
Slides/Handouts |
Homework |
1 |
9/1 Tue |
What is machine learning? Illustration
through linear models. |
HW0: Getting comfortable with python. |
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2 |
9/3 Thu |
Linear regression. Intro to basic elements of
ML: data, model, sampled training data, algorithm/training,
testing (aka generalization). |
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3 |
9/8 Tue |
Peek under the hood of the optimization
engine. Quick refresher of derivatives and gradients.
Gradient descent: the "natural" algorithm. |
Programming HW 1: Linear regression in python |
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4 |
9/10 Thu |
Modifying learning via changes to cost
function. Logistic and multiclass regression. l_2 and l_1
regularizers and their uses in preventing overfitting. (bias
towards sparsity). |
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5 |
9/15 Tue |
Quick tour of "datascience" through the
lens of linear models. k-fold cross validation.
Regression-based analysis in economics, and neuroscience.
Interpretability and its pitfalls. (Q&A with guest.) |
Programming HW 2: Data science explorations.
Effect of regularization. |
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6 |
9/17 Thu |
Unsupervised learning and its goals. Linear
algebra refresher (vectors, inner products, distance).
Clustering via k-means. |
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7 |
9/22 Tue |
Low-dimensional representation of high
dimensional data. Connections to eigenvalues/eigenvectors.
("SVD"); describe but not prove. Eigengenes and Eigenfaces. |
Programming HW 3: Clustering and
low-dimensional representation |
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8 |
9/24 Thu |
Refresher of elementary probability (coin
tossing, conditional probability and bayes rule). Simple
production models for text. n-grams. |
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9 |
9/29 Tue |
How to evaluate a probabilistic model of
text. Perplexity. Training vs test. (Detecting overfitting.)
|
Programming HW 4: n-gram model? matrix factorization? Genomics or neuroscience? |
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10 |
10/01 Thu |
Word embeddings and their fun properties.
Constructing word embeddings from n-gram models. (matrix
factorization) |
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11 |
10/06 Tue |
Lore of Gaussians. Understanding sampling and
generalization a bit better. (optional; may skip if running
late) The nonreproducibility problem in experimental social
science. |
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12 |
10/8 Thu |
Midterm review | Take-home midterm (timed) | ||
13 |
10/15 Thu |
Deep nets. Loss function and gradients. Chain rule | |||
14 |
10/20 Tue |
Backpropagation algorithm. | Programming HW 5: Implementing a simple deep net. | ||
15 |
10/22 Thu |
Convolutional nets and computer vision. | |||
16 |
10/27 Tue |
Convnets contd | Programming HW 6: ConvNets for vision. | ||
17 |
10/29 Thu |
Recurrent nets and applications.(optional;
can skip if running late) |
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18 |
11/03 Tue |
Decision-making under uncertainty: basic elements. (Rational choice framework; expected utility/cost) | No programming HW. |
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19 |
11/05 Thu |
Markov Decision Processes (MDP) as a model
for decision-making in a stochastic environment.Optimum
policy |
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20 |
11/10 Tue |
Computing optimum policy. (Policy
gradient) Guest minilecture by Elad Hazan on linear control
and ventilators. |
Programming HW7: Computing and using an optimum policy | ||
21 |
11/12 Thu |
(Guest lecture) Game-playing and Alpha Go. | |||
22 |
11/17 Tue |
Ethics in ML. Explainability, bias,
transparency, proper data collection. (Guest lecturer:
? Maybe Arvind) |
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23 |
11/19 Thu |
(Guest lecture) Deep Learning and Language Models | |||
24 |
11/24 |
Ask me anything. (Finals review during
reading period.) |