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schedule [2015/09/09 14:41] asa |
schedule [2016/10/04 09:39] asa |
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- | Video of the lectures will be available via the echo360 portal of the course | + | Video of the lectures is available via the echo360 portal of the course. A link is provided on Canvas and Piazza. |
- | |< 100% 17% 40% 20% 13% >| | + | |< 100% 18% 40% 19% 13% >| |
| ^ Topics ^ Reading ^ Assignments ^ | | ^ Topics ^ Reading ^ Assignments ^ | ||
- | ^ Week 1: August 25,27 | | | | | + | ^ Week 1: August 23,25 | | | | |
| Tuesday | Course introduction ({{wiki:01_intro.pdf | slides}}). | Sections 1.1 and 1.2 in the textbook | | | | Tuesday | Course introduction ({{wiki:01_intro.pdf | slides}}). | Sections 1.1 and 1.2 in the textbook | | | ||
- | | Thursday | Course introduction (continued). Linear models and the perceptron algorithm ({{wiki:02_linear.pdf | slides}}) | Chapters 1,3.1 in the textbook | | | + | | Thursday | Course introduction (continued). | Sections 1.1 and 1.2 in the textbook | [[assignments:assignment1| Assignment 1]] is available. | |
- | ^ Week 2: September 1,3 | | | | | + | ^ Week 2: August 30, Sept 1 | | | | |
- | | Tuesday | Linear models (continued). Short intro to python [ [[notes:python_getting_started | notes]] ] | Chapters 1,3.1 in the textbook | | | + | | Tuesday | Linear models ({{wiki:02_linear.pdf | slides}}). Short intro to LaTex and python [ [[notes:python_getting_started | notes]] ]. | Chapter 1, and Section 3.1 in the textbook | | |
- | | Thursday | More Python; [[code:perceptron | code]] for the perceptron. Linear regression ({{wiki:03_linear_regression.pdf | slides}}) | Chapter 3.2 | | | + | | Thursday | Linear models and the perceptron algorithm (cont). | Chapter 1, and Section 3.1 in the textbook | [[assignments:assignment2| Assignment 2]] is available. | |
- | ^ Week 3: September 8,10 | | | | | + | ^ Week 3: September 6,8 | | | | |
- | | Tuesday | Linear regression (continued). Intro to latex | Chapter 3.2 | | | + | | Tuesday | [[code:perceptron | code]] for the perceptron. Linear regression ({{wiki:03_linear_regression.pdf | slides}}). | Chapter 3.2 | | |
- | | Thursday | Logistic regression ({{wiki:04_logistic_regression.pdf | slides}}) | Chapter 3.3 | | | + | | Thursday | Logistic regression ({{wiki:04_logistic_regression.pdf | slides}}). | Chapter 3.3 | | |
+ | ^ Week 4: September 13,15 | | | | | ||
+ | | Tuesday | Overfitting ({{wiki:05_overfitting.pdf | slides}}) | Chapters 2.3,4.1 | | | ||
+ | | Thursday | Regularization and model selection ({{wiki:06_regularization.pdf | slides}}) | Chapter 4 | | ||
+ | ^ Week 5: September 20,22 | | | | | ||
+ | | Tuesday | Model selection and cross validation (continued). Code for [[code:cross_validation | cross validation]] in scikit-learn | Chapter 4 | [[assignments:assignment3| Assignment 3]] is available. | | ||
+ | | Thursday | Discussion of classifier evaluation and metrics for classifier accuracy; here's the code for computing/plotting [[code:roc|ROC curves]]. Short intro to large margin classification ({{wiki:07_svm.pdf | slides}}) | Chapter e-8 | | | ||
+ | ^ Week 6: September 27,29 | | | | | ||
+ | | Tuesday | Large margin classification: support vector machines ({{wiki:07_svm.pdf | slides}}) | Chapter e-8 | | | ||
+ | | Thursday | The dual for the hard margin and soft margin SVM ({{wiki:07_svm.pdf | slides}}); [[code:demo2d|svm demo]]; Expressing SVMs in terms of error + regularization ({{wiki:07_svm_unbalanced.pdf | slides}}) | Chapter e-8 | | | ||
+ | ^ Week 7: October 4,7 | | | | | ||
+ | | Tuesday | SVMs for unbalanced data ({{wiki:07_svm_unbalanced.pdf | slides}}) Nonlinear classification with kernels ({{wiki:08_kernels.pdf | slides}}) | Chapter e-8 | [[assignments:assignment4| Assignment 4]] is available. | | ||
+ | | Thursday | Kernels (continued) | Chapter e-8 | | | ||
+ | ... | ||
+ | |< 100% 18% 40% 19% 13% >| | ||
- | | + | ^ Week 15: December 6,8 | | | | |
+ | | Tuesday | Course summary | | | | ||
+ | | Thursday | Poster session | | | | ||
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