Topic schedule and notes

The slides, notes, and sample code shown in the class are posted here. The posted contents are intended as supplementary materials.

**You are expected to take notes during the class.**

**Please note that the schedule of topics is subject to change.**

- Wednesday, 2/6
- Introductions, syllabus, data, data visualization, why look at data? (Lecture01.pdf)
- Friday, 2/8
- Effective data visualization and human perception. (Lecture02.pdf)
**Homework 0**- See last slide from 2/6.

- Monday, 2/11
- Tkinter and building a GUI (Lecture03.pdf, lecture03_display_1_template_basic.py, lecture03_display_1_basic.py)
- Wednesday, 2/13
- 2D coordinate frame transformations, translation, scaling, linear algebra (Lecture04.pdf)
- Friday, 2/15
- Numpy, scientific computing, vectorization (Lecture05.pdf, lecture05_numpy_template.py, lecture05_numpy.py)
**Homework 1**- (HW01.pdf)

- Monday, 2/18
- Numpy broadcasting, 3D viewing, observer coordinates (Lecture06.pdf)
- Wednesday, 2/20
- Orthographic viewing pipeline, 3D viewing example (Lecture07.pdf)
- Friday, 2/22
- Finish orthographic viewing pipeline
**Homework 2**- (HW02.pdf)

- Monday, 2/25
- Interactive 3D viewing: panning/translation (Lecture09.pdf, lecture09_trackpad_tk_bindings.py)
- Wednesday, 2/27
- Interactive 3D viewing: rotation (Lecture10.pdf)
- Friday, 3/1
- Interactive 3D viewing: scaling (Lecture11.pdf)
**Homework 3**- (HW03.pdf)

- Monday, 3/4
- Histograms, kernel density estimation, probability (Lecture12.pdf)
- Wednesday, 3/6
- Binomial distribution, Bayesian updating (Lecture13.pdf, lecture_13_bayesian_updating_template.py)
- Friday, 3/8
- Review binomial distribution, Bayesian updating (lecture_13_bayesian_updating.py)
**Homework 4**- (HW04.pdf)

- Monday, 3/11
- Linear regression (Lecture15.pdf)
- Wednesday, 3/13
- Quality of fit, multiple linear regression (lecture16_linear_regression_template.py, lecture16_linear_regression.py)
- Friday, 3/15
- Writing workshop
**Homework 5**- (HW05.pdf)

- Monday, 3/18
- Gaussian distribution and covariance matrix (Lecture18.pdf, lecture_18_gaussian_binom_approx_template.py, lecture_18_gaussian_binom_approx.py lecture_18_gaussian_mult.py)
- Wednesday, 3/20
- Principal component analysis (PCA): Covariance approach (Lecture19.pdf, Board1, Board2, Board3 )
- Friday, 3/22
- Principal component analysis (PCA) (Board1, Board2, Board3 )
**Homework**- No HW this week!

- Monday, 3/25
- Spring break
- Wednesday, 3/27
- Spring break
- Friday, 3/29
- Spring break

- Monday, 4/1
- PCA code, data reconstruction, singular value decomposition (Lecture21.pdf, lecture_21_pca_cov_template.py, lecture_21_pca_cov.py Board1, Board2 )
- Wednesday, 4/3
- Finish SVD, clustering, distance metrics (Lecture22.pdf, Board1, Board2, Board3, Board4 )
- Friday, 4/5
- K-means clustering, Leader algorithm (Lecture23.pdf, Board1 )
**Homework 6**- (HW06.pdf)

- Monday, 4/8
- Hierarchical clustering, single-linkage algorithm, number of clusters (Board1, Board2, Board3 )
- Wednesday, 4/10
- Supervised learning and classification problems, K nearest neighbors (Lecture25.pdf, lecture24_hierarchical_clustering_template.py, lecture24_hierarchical_clustering.py, lecture24_elbow_fail.py, Board1, Board2, Board3 )
- Friday, 4/12
- Review: Bayes Rule (Lecture26.pdf, Board1, Board2 )
**Homework 7**- HW07.pdf

- Monday, 4/15
- Bayes Rule and Naive Bayes classification (Board1, Board2, Board3, Board4, Board5, lecture27_naive_bayes_1d.py)
- Wednesday, 4/17
- Classification performance metrics, confusion matrix (Lecture28.pdf, Board1, Board2, Board3 )
- Friday, 4/19
- More classification performance metrics, ROC curve (Board1, Board2 )
**Homework 8**- HW8.pdf

- Monday, 4/22
- C-index/AUC, No Free Lunch Theorem, Ugly Duckling Theorem (Lecture30.pdf, Board1, Board2, Board3 )
- Wednesday, 4/24
- Decision trees: overview, information entropy, information gain (Lecture31.pdf, Board1, Board2, Board3 )
- Friday, 4/26
- Decision trees: information gain, ID3 algorithm (Lecture32.pdf, Board1, Board2, Board3)
**Homework 9**- HW9.pdf

- Monday, 4/29
- Decision trees: ID3 algorithm, overfitting, pruning (Lecture33.pdf, Board1, Board2, Board3, Board4, )
- Wednesday, 5/1
- 1R decision trees, neural networks overview, McCulloch-Pitts neurons (Lecture34.pdf, Board1, Board2, Board3, Board4, Board5 )
- Friday, 5/3
- No class (CLAS)
**Homework**- No HW this week!

- Monday, 5/6
- McCulloch-Pitts neurons, single-layer supervised learning networks, ADALINE (Lecture35.pdf, Board1, Board2, Board3 )
- Wednesday, 5/8
- ADALINE and Perceptron learning, activation functions (lecture36_adaline.py, Board1, Board2, Board3 )
- Friday, 5/10
- XOR problem, universal approximation theorem, Multi-layer Perceptron, backpropagation algorithm (lecture37_adaline.py, Board1, Board2)
**Homework 10**- HW10.pdf

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