Day | Topic | Quiz | Class | Prep | Due |
---|---|---|---|---|---|
M 9/11 | Intro | C1 | P1 | ||
W 9/13 | Linear algebra, Matlab | Q2 | C2 | P2 | |
F 9/15 | Linear regression w/ one variable | Q3 | C3 | P3 | |
M 9/18 | Linear regression w/ multiple vars | Q4 | C4 | P4 | |
W 9/20 | Logistic regression | Q5 | C5 | P5 | |
F 9/22 | Regularization, NumPy | Q6 | HW 1 | – | |
M 9/25 | Logistic regression w/ regularization | C7 | P7 | HW 1 | |
W 9/27 | Neural nets 1 | Q8 | C8 | P8 | |
F 9/29 | Neural nets 2, backprop | Q9 | HW 2 | P9 | |
M 10/2 | Neural nets 3 | Q10 | C10 | P10 | |
W 10/4 | Bias vs. variance | Q11 | C11 | – | |
F 10/6 | HW 2 competition, catch up | R2 | P12 | HW 2 | |
M 10/9 | Precision and recall, HW 3 | Q13 | HW 3 | P13 | |
W 10/11 | Support vector machines 1 | Q14 | HW 3 | P14 | |
F 10/13 | SVMs, kernels, k-nearest neighbors | Q15 | C15 | P15 | |
M 10/16 | Stochastic gradient descent, SVMs | Q16 | C16 | P16 | |
W 10/18 | Midterm review | – | Midterm | ||
F 10/20 | No class | HW 3 | |||
M 10/23 | Fall Break | ||||
W 10/25 | SVMlight lab | Q18 | C18 | P18 | |
F 10/27 | K-means clustering | Q19 | C19 | P19 | |
M 10/30 | PCA | Q20 | C20 | P20 | |
W 11/1 | PCA 2, density estimation | Q21 | HW 4 | – | |
F 11/3 | HW 4 competition | Q22 | R4 | P22 | HW 4 |
M 11/6 | Anomaly detection, HW 5 | Q23 | HW 5 | P23 | |
W 11/8 | Recommender systems | Q24 | HW 5 | P24 | |
F 11/10 | Low rank matrix factorization | Q25 | C25 | P25 | |
M 11/13 | ML pipelines | Q26 | C26 | P26 | |
W 11/15 | HW 5, FP brainstorming | Q27 | R5 | P27 | HW 5 |
F 11/17 | FP proposal presentations | Q28 | teams | P28 | |
M 11/20 | Vectorized backprop | Q29 | C29 | P29 | FP proposals |
W 11/22 | Thanksgiving | ||||
F 11/24 | Thanksgiving | ||||
M 11/27 | |||||
W 11/29 | FP status | ||||
F 12/1 | |||||
M 12/4 | |||||
W 12/6 | FP presentations | ||||
F 12/8 | FP report |