CSE/STAT 416, Summer 2020: Introduction to Machine Learning

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
    Optional:
  • [Schafer] Python Review
  • [ESL] Section 1, 2.3.1
Lecture 2
(Wed, June 24)
Assessing Performance
Bias + Variance Tradeoff
  • Slides : pdf
  • Annotated : pdf
  • Train a Model : demo
  • Model Complexity : demo
    Optional:
  • [ESL] Section 2.3.1, 7.1-7.4
Section 1
(Thur, June 25)
Course Infrastructure / Pandas
  • Slides : pdf
  • Pandas Intro : demo
  • [Sol] Pandas Intro : demo
Week 2: Assessing Performance
Lecture 3
(Mon, June 29)
Regularization: Ridge
  • Slides : pdf
  • Annotated : pdf
  • Ridge Visualization : demo
    Optional:
  • [ESL] Section 3.1-3.2, 3.4.1
  • [ESL] Section 7.1-7.4
Lecture 4
(Wed, July 01)
Regularization: LASSO, Feature selection
  • Slides : pdf
  • Annotated : pdf
  • MLE Derivation : pdf
  • LASSO Visualization : demo
    Optional:
  • [Schafer] MLE Notes
  • [ESL] Section 2.9, 5.5.2, 7.2
  • [ESL] Section 3.4.2, 7.10
Section 2
(Thur, July 02)
Gradient Descent
  • Function Properties, Gradient Descent : demo

Case Study: Classification

Week 3: Classification
Lecture 5
(Mon, July 06)
Classification
  • Slides : pdf
  • Annotated : pdf
    Optional:
  • [ESL] Section 1, 2.3.1, 4.1-4.2
  • [FoML] Section 3.3
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
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
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
Section 5
(Thur, July 23)
Kaggle Setup
Precision/Recall + Local Methods
  • Kaggle Intro : demo
  • Bagging, Boosting, Precision, Recall : demo
Week 6: Clustering
Lecture 11
(Mon, July 27)
Clustering
  • Slides : pdf
  • Annotated : pdf
Lecture 12
(Wed, July 29)
Hierarchical Clustering
  • Slides : pdf
  • Annotated : pdf
  • Missing Data : pdf
  • Annotated : pdf
    Optional:
  • [Colab] Methods Review
  • [ESL] Section 14.3.12, 9.6
Section 6
(Thur, July 30)
Numpy, Variable Encoding, and Clustering
  • Slides : pdf
  • NumPy Tutorial : demo
  • Variable Encoding : demo

Case Study: Deep Learning

Week 7: Deep Learning
Lecture 13
(Mon, Aug 03)
Neural Networks
  • Slides : pdf
  • Annotated : pdf
Lecture 14
(Wed, Aug 05)
Deep Learning
Convolutional Neural Networks
  • Slides : pdf
  • Annotated : pdf
Section 7
(Thur, Aug 06)
Deep Learning
  • Slides : pdf
  • PyTorch Overview : demo

Case Study: Recommender Systems

Week 8: Recommender Systems
Lecture 15
(Mon, Aug 10)
PCA / Recommender Systems Intro
  • Slides : pdf
  • Annotated : pdf
Lecture 16
(Wed, Aug 12)
Recommender Systems / Matrix Factorization
  • Slides : pdf
  • Annotated : pdf
Section 8
(Thur, Aug 13)
PCA
Recommender Systems
Final Exam Review
Week 9: Wrap Up / Final Exam
Lecture 17
(Mon, Aug 17)
Explainability in Machine Learning / Ethics / Course Review
  • Slides : pdf
  • Annotated : pdf
Lecture 18
(Wed, Aug 19)
Final Exam (in class)
Section 9
(Thur, Aug 20)
No section