Announcements
Week 10+ Final grades have been submitted for this course. Congratulations on finishing the summer as machine learning practioners! For any grade-related questions, contact the teaching staff at cse416staff@u.washington.edu.
Instructor Vinitra Swamy, Summer 2020
If you have inquiries about the course or would like to explore the machine learning curriculum further, reach out to Vinitra at vinitra@cs.washington.edu.
Affiliation University of Washington, Seattle
Calendar
Day
Topic
Materials
References
Assignments
Case Study: Regression
Week 1: Introduction / Regression
Lecture 1
(Mon, June 22)
Linear Regression
- Slides : pdf
- Annotated : pdf
- Homework 0 Out
Lecture 2
(Wed, June 24)
Assessing Performance
Bias + Variance Tradeoff
- Optional:
- [ESL] Section 2.3.1, 7.1-7.4
- Homework 1 Out
Section 1
(Thur, June 25)
Course Infrastructure / Pandas
Week 2: Assessing Performance
Lecture 3
(Mon, June 29)
Regularization: Ridge
- Slides : pdf
- Annotated : pdf
- Ridge Visualization : demo
Lecture 4
(Wed, July 01)
Regularization: LASSO, Feature selection
- Slides : pdf
- Annotated : pdf
- MLE Derivation : pdf
- LASSO Visualization : demo
- Homework 2 Out
Case Study: Classification
Week 3: Classification
Lecture 5
(Mon, July 06)
Classification
- Slides : pdf
- Annotated : pdf
Lecture 6
(Wed, July 08)
MLE / Logistic Regression
- Slides : pdf
- Annotated : pdf
- Sigmoid Function : demo
- Optional:
- [ESL] Section 4.4.1-4.4.4, 9.1.2, 7.5-7.6
- Homework 3 Out
Section 3
(Thur, July 09)
Classification / Logistic Regression
- Slides : pdf
- Problems : pdf
- Logistic Regression : demo
Week 4: Trees
Lecture 7
(Mon, July 13)
Naive Bayes / Decision Trees
- Slides : pdf
- Annotated : pdf
Lecture 8
(Wed, July 15)
Ensemble Methods
- Slides : pdf
- Annotated : pdf
- Optional:
- [ESL] Section 9.2.4, 10.1-10.10
- Deriving AdaBoost
- Explaining AdaBoost (Schapire 2013)
- Homework 4 Out
Section 4
(Thur, July 16)
Trees and Ensemble Models
- Slides : pdf
- Gini Impurity : pdf
- Random Forest : demo
Case Study: Clustering and Similarity
Week 5: Non-Parametric Methods
Lecture 9
(Mon, July 20)
Precisions + Recall / kNN
- Slides : pdf
- Annotated : pdf
Lecture 10
(Wed, July 22)
Kernel Methods
Locality Sensitive Hashing
- Slides : pdf
- Annotated : pdf
- Optional:
- Slides on Approximate NN
- Homework 5 Out
Section 5
(Thur, July 23)
Kaggle Setup
Precision/Recall + Local Methods
Week 6: Clustering
Lecture 11
(Mon, July 27)
Clustering
- Slides : pdf
- Annotated : pdf
- Optional:
- [ESL] Section 13.2.1, 14.3.6, 14.3.11
- k-means Viz
Lecture 12
(Wed, July 29)
Hierarchical Clustering
- Slides : pdf
- Annotated : pdf
- Missing Data : pdf
- Annotated : pdf
- Homework 6 Out
Section 6
(Thur, July 30)
Numpy, Variable Encoding, and Clustering
Case Study: Deep Learning
Week 7: Deep Learning
Lecture 13
(Mon, Aug 03)
Neural Networks
- Slides : pdf
- Annotated : pdf
- Optional:
- Neural Networks
- Compute any Function
Lecture 14
(Wed, Aug 05)
Deep Learning
Convolutional Neural Networks
- Slides : pdf
- Annotated : pdf
- Homework 7 Out
Case Study: Recommender Systems
Week 8: Recommender Systems
Lecture 15
(Mon, Aug 10)
PCA / Recommender Systems Intro
- Slides : pdf
- Annotated : pdf
- Optional:
- PCA Visualized
- [FoML] 15, 15.1
- t-SNE Explained
Lecture 16
(Wed, Aug 12)
Recommender Systems / Matrix Factorization
- Slides : pdf
- Annotated : pdf
- Homework 8 Out
Week 9: Wrap Up / Final Exam
Lecture 17
(Mon, Aug 17)
Explainability in Machine Learning / Ethics / Course Review
- Slides : pdf
- Annotated : pdf
- Optional:
- Interpretable Machine Learning
Lecture 18
(Wed, Aug 19)
Final Exam (in class)
Section 9
(Thur, Aug 20)
No section
