Thu., September 25:

• Lecture: What is learning? Point estimation and MLE, Gaussians. [Slides] [Annotated Slides]
• Additional Reference: Andrew Moore's basic probability tutorial
• Readings: Murphy 1.1 - 1.4, 2.4.1

Tue., September 30:

• Lecture: Linear regression, bias-variance tradeoff. [Slides] [Annotated Slides]
• Readings: Murphy 7.1-7.3, 6.4, 7.5.1

Wed., October 1:

• Optional: Python tutorial. EEB 045 5:00pm [Code and dataset]

Thu., October 2:

• Lecture: Overfitting, regularization, ridge regression, cross-validation. [Slides] [Annotated Slides]
• Readings: Murphy 6.5.3

Tue., October 7:

• Lecture: Variable selection, sparsity, LASSO. [Slides] [Annotated Slides]
• Readings: Murphy 13.1, 13.3-13.4.1

Wed., October 8:

• Recitation: Bias/Variance, Linear Regression, LASSO. EEB 045 5:00pm [Bias/Variance Demo (Python)]

Thu., October 9:

• Lecture: Classification, logistic regression, gradient descent. [Slides] [Annotated Slides]
• Readings: Murphy 8.1 - 8.3, 8.5.2

Tue., October 14:

• Lecture: Stochastic gradient descent, boosting. [Boosting Slides] [Annotated Slides]
• Readings: Murphy 16.4

Wed., October 15:

• Recitation: Adaboost, Logistic Regression. EEB 045 5:00pm

Thu., October 16:

• Lecture: Decision trees. [Decision Trees Slides] [Annotated Slides]
• Readings: Murphy 16.2

Tue., October 21:

• Lecture: Decision trees, continued. Instance-based learning, nearest neighbors. [Instance-based Slides] [Instance-based Annotated Slides] [Decision Trees continued, Annotated Slides]

Wed., October 22:

• Recitation: Introduction to Boosted Trees, EEB 045, 5:00pm [Slides]

Thu., October 23:

• Lecture: Online learning, perceptron, hinge loss. [Slides] [Annotated Slides]
• Readings: Murphy 8.5.0, 8.5.4, 14.4

Tue., October 28:

• Lecture: Kernels, SVMs. [Slides] [Annotated Slides]
• Readings: Murphy 14.5

Wed., October 22:

• Recitation: Decision Trees, Midterm Review, EEB 045, 5:00pm

Thu., October 30:

• IN CLASS - Midterm

Tue., November 4:

• Lecture: SVMs, continued. Clustering, K-means. [Slides] [Annotated SVMs Slides] [Annotated Clustering Slides]
• Readings: Murphy 11.4.2.5

Wed., November 5:

• Recitation: SVM [Slides]

Thu., November 6:

• Lecture: Mixtures of Gaussians, EM. [Slides] [Annotated Slides]
• Readings: Murphy 11.2, 11.4 - 11.4.2.3

Tue., November 11:

• NO CLASS - Veterans Day

Wed., November 12:

• Recitation: K-means/GMM, EEB 045 5:00pm

Thu., November 13:

• Lecture: EM Continued, dimensionality reduction, PCA, SVD. [Slides] [Annotated Slides]
• Readings: Murphy 12.1.0, 12.2

Tue., November 18:

• Lecture: Naive Bayes, Bayesian Networks. [Slides] [Annotated Slides]
• Readings: Murphy 3.5, 10.1 - 10.2.2

Thu., November 20:

• Lecture: Bayesian Networks continued, Bayes net structure learning. [Slides] [Annotated Slides]
• Readings: Murphy 10.3

Tue., November 25:

• Lecture: Bayes nets structure learning, continued. Learning Theory. [Learning Theory Slides] [Annotated Structure Learning Slides] [Annotated Learning Theory Slides]
• Readings: Murphy 6.5.4

Wed., November 26:

• Recitation: No recitation

Thu., November 27:

• NO CLASS - THANKSGIVING

Tue. December:

• Lecture: Neural networks, convolutional neural networks for computer vision. [Slides]