CSCI-567: Machine Learning
Spring 2019
This schedule is meant as an outline. Depending on progress, material may be added or removed. Also, there will often be interesting tangents to follow.
Date | Topics Covered | Discussion | |
---|---|---|---|
1 | Jan. 07 – 11 | Course Overview & kNN Cross-validation Leave-one-out |
dis. 1 |
2 | Jan. 14 – 18 | Decision Tree & Naive Bayes Entropy and Gini impurity Reduced-Error Pruning Naive Bayes assumption PA1 due Jan. 25 |
dis. 2 |
3 | Jan. 21 – 25 | Linear Regression Residual Sum of Squares Nonlinear basis Regularization TA1 due Feb. 01 |
dis. 3 |
4 | Jan. 28 – Feb. 01 | Perceptron & Logistic Regression Gradient Descent Surrogate Losses Multiclass Classification PA2 due Feb. 08 |
dis. 4 |
5 | Feb. 04 – 08 | Neural Networks Backpropagation Preventing overfitting TA2 due Feb. 15 |
dis. 5 |
6 | Feb. 11 – 15 | Convolutional Neural Networks PA3 due Mar. 10 |
dis. 6 |
7 | Feb. 18 – 22 | Kernels & Clustering Mercer Theorem Kernelizing ML algorithms K-means clustering |
dis. 7 |
8 | Feb. 25 – Mar. 01 | Review for exam Exam – I |
no discussions |
9 | Mar. 04 – 08 | Support Vector Machines Linear Programming Lagrangian Duality KKT conditions Dual SVM TA3 due Mar. 24 |
dis. 8 |
10 | Mar. 18 – 22 | Boosting & Gaussian Mixture Models AdaBoost |
dis. 9 |
11 | Mar. 25 – 29 | Gaussian Mixture Models EM algorithm Density estimation TA4 due Apr. 19 |
dis. 10 |
12 | Apr. 01 – 05 | Hidden Markov Models Markov chains Viterbi algorithm PA4 due Apr. 21 |
dis. 11 |
13 | Apr. 08 – 12 | HMM & PCA Baum-Welch algorithm PCA algorithm |
dis. 12 |
14 | Apr. 15 – 19 | Reinforcement Learning E.Brunskill notes Multi-Armed Bandits Markov Decision Processes Bellman's optimality principle |
no discussions |
15 | Apr. 22 – 26 | Review for exam Exam – II |
no discussions |