Introduction to Machine Learning (original) (raw)
Calendar
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
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:
Regularization: LASSO, Feature selection
Optional:
Section 2
(Thur, July 04)
4th of July. No class.
Case Study: Classification
Classification
- Slides :pdf
Optional:
- [ESL] Section 1, 2.3.1, 4.1-4.2
Section 3
(Thur, July 11)
Classification / Logistic Regression
Decision Trees
- Slides :pdf
Section 4
(Thur, July 18)
Trees and Ensemble Models
Case Study: Clustering and Similarity
Week 5: Non-Parametric Methods
Precisions+Recall / kNN
- Slides :pdf
Section 5
(Thur, July 25)
Kaggle Setup
Precision/Recall + Local Methods
Lecture 11
(Mon, July 29)
Clustering
- Slides :pdf
Optional:
- [ESL] Section 13.2.1, 14.3.6, 14.3.11
- k-means Viz
Lecture 12
(Wed, July 31)
Hierarchical Clustering
Optional:
[ESL] Section 14.3.12, 9.6
Numpy and Clustering
Case Study: Deep Learning
Deep Learning
Case Study: Recommender Systems
Week 8: Recommender Systems
PCA
Recommender Systems
Final Exam Review
Week 9: Wrap Up / Final Exam