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.
Week 1
- 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.
Week 2
- 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)
Week 3
- 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)
Week 4
- 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)
Week 5
- 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)
Week 6
- 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)
Week 7
- 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!
Week 8
- Monday, 3/25
- Spring break
- Wednesday, 3/27
- Spring break
- Friday, 3/29
- Spring break
Week 9
- 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)
Week 10
- 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
Week 11
- 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
Week 12
- 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
Week 13
- 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!
Week 14
- 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
Final study guide (Section B)
- FinalTopics.pdf
Stephanie's notes (Section A)
- Notes PDF (Spring 2019)
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