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STATS 315B

Calendar

Week 1 - Course Intro

Apr 3
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
Apr 5
Lecture 2 Reiview II
HW 0 due (4/7 Fri) HW 1 out
CQ1 out: Intro
  • 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 10
Lecture 3 Decision trees I
 
  • Decision trees, Overfitting in decision trees
  • Reading:
    1. PML1 18.1
    2. (Optional) ESL 9.2.1-9.2.3
Apr 12
Lecture 4 Decision trees II
 
CQ1 due: Intro
  • 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 17
Lecture 5 Decision trees III
 
  • AdaBoost cont’d, Gradient boosting
  • Reading:
    1. PML1 5.1.4
Apr 19
Lecture 6 Decision trees IV
HW 1 due HW 2 out
CQ2 out: Decision trees, boosting, random forests
  • Gradient boosting cont’d, Random forests, Precision and recall
  • Reading:
    1. PML1 5.1.4
Apr 24
Lecture 7 Deep learning I
 
  • Deep learning intro, Single and multilayer networks
  • Reading:
    1. PML2 16.1-16.3
Apr 26
Lecture 8 Deep learning II
 
CQ2 due: Decision trees, boosting, random forests
  • Back propagation
  • Reading:
    1. PML2 16.4
May 1
Lecture 9 Deep learning III
Project Proposal due
  • Convnets, Transfer learning
  • Reading:
    1. See L6 reading

Week 5, 6, 7, 8 - Unsupervised Learning

May 3
Lecture 10 Clustering I
HW 2 due HW 3 out
CQ3 out: Deep learning
  • Clustering and unsupervised learning, K-means, Mixure models, Gaussians
  • Reading:
    1. PML1 21.1, 21.3
    2. (Optional) ESL 14.3.6
May 8
Lecture 11 Clustering II
 
  • Mixture of Gaussians, EM algorithm for MoG
  • Reading:
    1. PML1 21.4, 8.7
    2. PML2 29.2, 6.6.3-6.6.4
May 10
Lecture 12 Clustering III
 
CQ3 due: Deep learning
  • EM more generally, Connection to K-means, Hierarchical clustering intro
  • Reading:
    1. ESL 14.3.7, 14.3.12
    2. PML1 21.2
May 15
Lecture 13 Latent embeddings I
 
  • Hierarchical clustering, PCA
  • 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 17
Lecture 14 Latent embeddings II
HW 3 due HW 4 out
CQ4 out: K-means, MoG, EM, Hierarchical clustering
  • PCA more formally, Factor analysis
  • Reading:
    1. PML1 20.3
    2. PML2 21.1-21.3, 22.1-22.2
May 22
Lecture 15 Matrix Factorization I
Project Midway due
  • EM algorithm for FA, Autoencoders, VAEs
  • Reading:
    1. PML1 22.1
May 24
Lecture 16 Matrix Factorization II
 
CQ4 due: K-means, MoG, EM, hierarchical clustering
  • VAEs cont’d; Recommender systems intro, Collaborative filtering via matrix factorization
  • Reading:
    1. PML2 16.3.5, 29.5.1
    2. (Optional) ESL 14.6

Week 9, 10 - Time Series

May 31
Lecture 17 Time Series I
HW 4 due
CQ5 out: PCA, autoencoders, MF&FA, HMMs
June 5
Lecture 18 Time Series II
 
June 7
Lecture 19 Time Series III
 
CQ5 due: PCA, autoencoders, MF&FA, HMMs