Link Search Menu Expand Document
STATS 315B

Calendar

Week 1 - Course Intro

Mar 29
Lecture 1 Review I
HW 0 out
  • Point estimation, MLE, Linear regression and classification
  • Reading:
    1. PML1 1.1-1.4, 11.1-11.2, 10.1-10.2
    2. (Optional) ESL 1, 2.3.1, 3.1-3.2, 4.1, 4.4.1-4.4.2
Mar 31
Lecture 2 Reiview II
 
  • Assessing performance, Overfitting, Bias-variance tradeoff
  • Reading:
    1. ESL 7.1-7.4
    2. (Optional) PML1 4.7.6

Week 2, 3, 4 - Non-linear regression and classification

Apr 5
Lecture 3 Decision trees I
HW 0 due HW 1 out
CQO: intro
  • Decision trees
  • Reading:
    1. PML1 18.1
    2. (Optional) ESL 9.2.1-9.2.3
Apr 7
Lecture 4 Decision trees II
 
  • Decision trees, Boosting, Adaboost
  • Reading:
    1. PML1 18.4-18.5
    2. (Optional) ESL 9.2.4, 10.1-10.10, 15.1-15.4,
    3. (Optional) Explaining Adaboost
Apr 12
Lecture 5 Decision trees III
 
CQ1: decision trees
  • AdaBoost cont’d, gradient boosting
  • Reading:
    1. PML1 5.1.4
Apr 14
Lecture 6 Decision trees IV
 
  • Gradient boosting cont’d, random forests, missing data, precision and recall
  • Reading:
    1. PML1 5.1.4
Apr 19
Lecture 7 Deep learning I
HW 1 due HW 2 out
  • Deep learning intro, single and multilayer networks
  • Reading:
    1. PML2 16.1-16.3
Apr 21
Lecture 8 Deep learning II
 
  • Back propagation
  • Reading:
    1. PML2 16.4
Apr 26
Lecture 9 Deep learning III
Project Proposal due
CQ2: deep learning
  • Convnets, Transfer learning
  • Reading:
    1. see L6 reading

Week 5, 6, 7, 8 - Unsupervised Learning

Apr 28
Lecture 10 Clustering I
 
  • Clustering and unsupervised learning, K-means clustering
  • Reading:
    1. PML1 21.1, 21.3
    2. (Optional) ESL 14.3.6
May 3
Lecture 11 Clustering II
HW 2 due HW 3 out
  • Mixture of Gaussians, EM algorithm
  • Reading:
    1. PML1 21.4, PML2 29.2, PML1 8.7
    2. PML2 6.6.3-6.6.4
May 5
Lecture 12 Clustering III
 
  • EM algorithm, Connection to K-means, Hierarchical clustering
  • Reading:
    1. ESL 14.3.7, 14.3.12
    2. PML1 21.2
May 10
Lecture 13 Latent embeddings I
 
CQ3: clustering, EM, PCA
  • PCA, Factor analysis, EM algorithm for Factor analysis
  • Reading:
    1. PML1 20.1 20.2
    2. PML2 29.3, 29.6
    3. (Optional) ESL 14.5.1, 14.7.1-14.7.2
May 12
Lecture 14 Latent embeddings II
Project Midway due
  • Autoencoders, Variational autoencoders
  • Reading:
    1. PML1 20.3
    2. PML2 21.1-21.3, 22.1-22.2
May 17
Lecture 15 Matrix Factorization I
HW 3 due HW 4 out
  • Collaborative filtering via matrix factorization
  • Reading:
    1. PML1 22.1
May 19
Lecture 16 Matrix Factorization II
 
  • NMF, Probabilistic matrix factorization, Performance metrics, Graph NN
  • Reading:
    1. PML2 16.3.5, 29.5.1
    2. (Optional) ESL 14.6

Week 9, 10 - Time Series

May 24
Lecture 17 Time Series I
 
CQ4: autoencoders, matrix factorization
May 26
Lecture 18 Time Series II
 
May 31
Lecture 19 Time Series III
HW 4 due
CQ5: SSMs, deep learning for sequence data