Note: This is a rough sketch of the quarter that is likely to change. We can accurately predict the past, but predicting the future is hard!
Case Study: Regression
Week 1: Introduction / Regression
Linear Regression
Optional:
-
[Schafer] Python Review
-
[ESL] Section 1, 2.3.1
Assessing Performance
Bias + Variance Tradeoff
Optional:
-
[ESL] Section 2.3.1, 7.1-7.4
Section 1
(Thur, June 27)
Course Infrastructure / Pandas
Week 2: Assessing Performance
Regularization: Ridge
Optional:
-
[ESL] Section 3.1-3.2, 3.4.1
-
[ESL] Section 7.1-7.4
Regularization: LASSO, Feature selection
Optional:
-
[ESL] Section 2.9, 5.5.2, 7.2
-
[ESL] Section 3.4.2, 7.10
Section 2
(Thur, July 04)
4th of July. No class.
Case Study: Classification
Classification
Optional:
-
[ESL] Section 1, 2.3.1, 4.1-4.2
MLE / Logistic Regression
Optional:
-
[ESL] Section 4.4.1-4.4.4, 9.1.2, 7.5-7.6
Section 3
(Thur, July 11)
Classification / Logistic Regression
Section 4
(Thur, July 18)
Trees and Ensemble Models
Case Study: Clustering and Similarity
Week 5: Non-Parametric Methods
Lecture 10
(Wed, July 24)
Kernel Methods
Locality Sensitive Hashing
Section 5
(Thur, July 25)
Kaggle Setup
Precision/Recall + Local Methods
Lecture 11
(Mon, July 29)
Clustering
Lecture 12
(Wed, July 31)
Hierarchical Clustering
-
Slides
:
pdf
-
Missing Data
:
pdf
Optional:
-
[ESL] Section 14.3.12, 9.6
Numpy and Clustering
-
Handout
:
pdf
-
Solution
:
pdf
-
Numpy Demo
:
pdf
-
Numpy Demo Solution
:
pdf
Case Study: Deep Learning
Deep Learning
Convolutional Neural Networks
Case Study: Recommender Systems
Week 8: Recommender Systems
PCA / Recommender Systems Intro
Recommender Systems / Matrix Factorization
PCA
Recommender Systems
Final Exam Review
Week 9: Wrap Up / Final Exam
Online Learning / Course Wrap Up
-
Online Learning Slides
:
pdf
-
Course Wrap-up Slides
:
pdf