CSE/STAT 416

Welcome to CSE/STAT 416: Introduction to Machine Learning! 🎉

Registration

Do not email the course staff or instructor requesting an add-code for the course. The course staff do not have any add-codes. Please see the Registration FAQ for answers to common registration questions.

Announcements¶

Mar 27

Welcome to CSE/STAT 416 🎉

Information about the class posted including:

Links to the course website (here) and other resources
Information about the class structure. No pre-class work for the first class.
Office hours start Monday, April 1^st.

See the full announcement on Ed!

This Week (at a glance)¶

Monday (04/29)

Learning Reflection 4 Due

Wednesday (05/01)

Neural Networks

Thursday (05/02)

Homework 3 Due

Friday (05/03)

Convolutional Neural Networks

Calendar¶

Info

This is a rough sketch of the quarter and things are subject to change. We can accurately predict the past, but predicting the future is hard!

Lessons

Anything listed in the “Lesson” materials for a day should be done before attending class that day. We recommend doing all the slides before the “Pause and Think” slide. Each class session will start by reviewing what was in the Lesson and then most time will be spent on working on practice problems in the Lessons. See the syllabus for more info!

	Topic	Homeworks	Learning Reflections
Module 0 - Introduction / Regression
Wed 03/27	LES 00 Regression Note: Normally there is are pre-class materials that you should complete before attending class. For the first day there are none! You should complete the Checkpoint after class (due before the next class). Note (2): The CSE/STAT 416 Training links for today are extremely useful resources to make sure you are prepared for the class. The course will be in Python, so Resources 1 and 2 will be very helpful if you are less familiar with the language. Resource 3 covers the mathematical background we expect students to be comfortable with in the course. You are expected to complete these trainings, but they are not factored into your grade. We encourage you to work on them now before assignments are released. pre-class: None megathread in-class: pdf annotated pptx post-class: checkpoint resources: videos extra resources Videos Recording Extra resources [Schafer] AnIML - Introduction (draft) Note: This is a draft book for the course. It is still a work in progress so it will still have some errors and TODOs left throughout. Feedback is welcome and encouraged! [Schafer] AnIML - Linear Regression (draft) [Nguyen] Previous Quarter Lecture Notes Note: These notes are from a different offering of the course and may use slightly different notation and terminology. [ESL] Section 1, 2.3.1 Note: The Elements of Statistical Learning [ESL] is an advanced machine learning textbook that we link to as optional readings for students who want to learn more of the nitty-gritty details behind the model's derivations. You do not need to understand readings labeled as "Optional" CSE/STAT 416 Training: 1) Python Practice, 2) numpy Practice, 3) Math/Probability/Statistics Practice
Wed 03/27			Out LR0 Due 11:59 pm
Thu 03/28	SEC 00 Course Infrastructure; Pandas resources: handout
Thu 03/28	SEC 00 Course Infrastructure; Pandas resources: handout
Fri 03/29	LES 01 Assessing Performance; Bias + Variance Tradeoff pre-class: lesson megathread in-class: pdf annotated pptx demo post-class: checkpoint resources: videos extra resources advanced resources Videos Recording Extra resources [Schafer] AnIML - Assessing Performance (draft) [Nguyen] Lecture Notes from previous quarter Advanced resources [ESL] Section 2.3.1, 7.1-7.4
Fri 03/29		Out HW0 House Prices Due 11:59 pm
Mon 04/01
Mon 04/01
Tue 04/02
Tue 04/02
Module 1 - Assessing Performance
Wed 04/03	LES 02 Regularization: Ridge pre-class: lesson megathread in-class: pdf annotated pptx demo post-class: checkpoint resources: videos extra resources advanced resources Videos Recording Extra resources [Schafer] AnIML - Ridge Regression (draft) [Nguyen] Lecture Notes from previous quarter Advanced resources [ESL] Section 3.1-3.2, 3.4.1 [ESL] Section 7.1-7.4
Wed 04/03			Out LR1 Due 11:59 pm
Thu 04/04	SEC 01 Ridge and LASSO; Code resources: handout
Thu 04/04	SEC 01 Ridge and LASSO; Code resources: handout
Fri 04/05	LES 03 Regularization: LASSO, Feature selection pre-class: lesson megathread in-class: pdf annotated pptx demo post-class: checkpoint resources: videos extra resources advanced resources Videos Recording Extra resources [Nguyen] Lecture notes from previous quarter [Schafer] AnIML - LASSO and Feature Selection Advanced resources [ESL] Section 2.9, 5.5.2, 7.2 [ESL] Section 3.4.2, 7.10
Fri 04/05		Out HW1 Ridge and LASSO Due 11:59 pm
Mon 04/08
Mon 04/08
Tue 04/09
Tue 04/09
Module 2 - Classification
Wed 04/10	LES 04 Classification pre-class: lesson megathread in-class: pdf annotated pptx post-class: checkpoint resources: videos extra resources advanced resources Videos Recording Extra resources [Nguyen] Lecture notes from previous quarter [Schafer] AnIML - Classification Advanced resources [ESL] Section 1, 2.3.1, 4.1-4.2
Wed 04/10			Out LR2 Due 11:59 pm
Thu 04/11	SEC 02 Classification ; Logistic Regression resources: handout
Thu 04/11
Fri 04/12	LES 05 MLE / Logistic Regression pre-class: lesson megathread in-class: pdf annotated pptx demo post-class: checkpoint resources: videos extra resources advanced resources Videos Recording Extra resources [Nguyen] Lecture notes from a previous quarter [Schafer] AnIML - Logistic Regression Advanced resources [ESL] Section 4.4.1-4.4.4, 9.1.2, 7.5-7.6
Fri 04/12		Out HW2 Sentiment Analysis with Logistic Regression Due 11:59 pm
Mon 04/15
Mon 04/15
Tue 04/16
Tue 04/16
Module 3 - Societal Impact, Bias, and Fairness
Wed 04/17	LES 06 Bias and Fairness pre-class: lesson megathread in-class: pdf annotated pptx post-class: checkpoint megathread resources: videos extra resources advanced resources Videos Recording Extra resources [Schafer] AnIML - Bias and Fairness (7.1-7.3) Advanced resources People and AI Research (PAIR, Google) ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT) A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle (Suresh & Guttag 2019)
Wed 04/17			Out LR3 Due 11:59 pm
Thu 04/18	SEC 03 Midterm Review resources: handout
Thu 04/18	SEC 03 Midterm Review resources: handout
Fri 04/19	LES 07 Fairness and Tradeoffs ; Recap pre-class: lesson megathread in-class: pdf annotated pptx post-class: checkpoint megathread resources: videos extra resources advanced resources Videos Recording Extra resources [Schafer] AnIML - Fairness and Tradeoffs (7.3-7.6) Advanced resources The Ethical Algorithm - Michael Kearns & Aaron Roth On the (im)possibility of fairness (Friedler et al. 2016)
Fri 04/19
Mon 04/22
Mon 04/22		Out EXAM Midterm Due 11:59 pm
Tue 04/23
Tue 04/23
Module 4 - Trees/Ensemble Methods
Wed 04/24	LES 08 Naive Bayes / Decision Trees pre-class: lesson megathread in-class: pdf annotated pptx post-class: checkpoint megathread resources: videos extra resources Videos Recording Extra resources [Schafer] AnIML - Naïve Bayes [Schafer] AnIML - Decision Trees [Nguyen] Lecture notes from a previous quarter
Wed 04/24			Out LR4 Due 11:59 pm
Thu 04/25	SEC 04 Trees and Ensemble Methods resources: handout
Thu 04/25	SEC 04 Trees and Ensemble Methods resources: handout
Fri 04/26	LES 09 Ensemble Methods pre-class: lesson megathread in-class: pdf annotated pptx post-class: checkpoint megathread resources: videos extra resources advanced resources Videos Recording Lecture Supplement Extra resources [Schafer] AnIML - Ensemble Methods (coming soon) [Nguyen] Lecture notes from a previous quarter Advanced resources Deriving AdaBoost Explaining AdaBoost (Schapire 2013) [ESL] Section 9.2.4, 10.1-10.10
Fri 04/26		Out HW3 Loan Safety with Decision Trees Due 11:59 pm
Mon 04/29
Mon 04/29
Tue 04/30
Tue 04/30
Module 5 - Deep Learning
Wed 05/01	LES 10 Neural Networks pre-class: lesson in-class: pdf annotated pptx post-class: checkpoint resources: videos extra resources advanced resources Videos Recording Extra resources [Schafer] AnIML - Neural Networks (coming soon) [Nguyen] Lecture notes from a previous quarter Playground Advanced resources Neural Network Notes Backpropogation Algorithm Proof of Approximating and Function
Wed 05/01			Out LR5 Due 11:59 pm
Thu 05/02	SEC 05 Deep Learning resources: handout
Thu 05/02	SEC 05 Deep Learning resources: handout
Fri 05/03	LES 11 Deep Learning; Convolutional Neural Networks pre-class: lesson megathread in-class: pdf annotated pptx post-class: checkpoint resources: videos extra resources advanced resources Videos Recording Extra resources [Schafer] AnIML - Convolutional Neural Networks (coming soon) [Nguyen] Lecture notes from a previous quarter What is a Convolutional Neural Network? Advanced resources CS231n: Convolutional Neural Networks for Visual Recognition
Fri 05/03		Out HW4 Deep Learning with PyTorch Due 11:59 pm
Mon 05/06
Mon 05/06
Tue 05/07
Tue 05/07
Module 6 - Non-Parametric Methods
Wed 05/08	LES 12 Precision + Recall / kNN pre-class: lesson megathread in-class: pdf annotated pptx demo post-class: checkpoint resources: videos extra resources
Wed 05/08			Out LR6 Due 11:59 pm
Thu 05/09	SEC 06 Kaggle Setup
Thu 05/09	SEC 06 Kaggle Setup
Fri 05/10	LES 13 Kernel Methods; Locality Sensitive Hashing pre-class: lesson megathread in-class: pdf annotated pptx demo post-class: checkpoint resources: videos extra resources
Fri 05/10		Out HW5 ML Practice on Kaggle Due 11:59 pm
Mon 05/13
Mon 05/13
Tue 05/14
Tue 05/14
Module 7 - Clustering
Wed 05/15	LES 14 Clustering pre-class: lesson megathread in-class: pdf annotated pptx demo post-class: checkpoint resources: videos extra resources
Wed 05/15			Out LR7 Due 11:59 pm
Thu 05/16	SEC 07 Numpy ; Variable Encoding ; Clustering
Thu 05/16	SEC 07 Numpy ; Variable Encoding ; Clustering
Fri 05/17	LES 15 Hierarchical Clustering pre-class: lesson megathread in-class: pdf annotated pptx demo post-class: checkpoint resources: videos extra resources
Fri 05/17		Out HW6 k-means with Text Data Due 11:59 pm
Mon 05/20
Mon 05/20
Tue 05/21
Tue 05/21
Module 8 - Recommender Systems
Wed 05/22	LES 16 PCA / Recommender Systems Intro pre-class: lesson megathread in-class: pdf annotated pptx demo post-class: checkpoint resources: videos extra resources
Wed 05/22			Out LR8 Due 11:59 pm
Thu 05/23	SEC 08 PCA ; Recommender Systems
Thu 05/23	SEC 08 PCA ; Recommender Systems
Fri 05/24	LES 17 Recommender Systems / Matrix Factorization pre-class: lesson megathread in-class: pdf annotated pptx demo post-class: checkpoint resources: videos extra resources
Fri 05/24		Out HW7 Tweet Topic Modelling Due 11:59 pm
Mon 05/27
Mon 05/27
Tue 05/28
Tue 05/28			Out LR9 Due 11:59 pm
Module 9 - Course Wrap Up
Wed 05/29	LES 18 Course Wrap Up; Next Steps; Generative AI pre-class: lesson megathread in-class: pdf annotated pptx demo post-class: checkpoint resources: videos extra resources
Wed 05/29
Thu 05/30	SEC 09 Final Review
Thu 05/30	SEC 09 Final Review
Fri 05/31
Fri 05/31
Mon 06/03
Mon 06/03		EXAM Final Exam At 6:00 PM