Schedule

Warning

Since this course is new, please expect that the schedule will change to accomodate your learning!

Monday	Wednesday	Thursday
02 September Labor Day: no classes.	04 September Topic: What is Probabilistic ML? [slides] Vectorization Pre-Class: Read The Fear of Publicly Not Knowing Read the course syllabus Join the course on Gradescope Answer the syllabus survey on Gradescope by 5pm the day before---it's graded. Released: HW1: Vectorization	05 September Topic: Probability (Discrete) [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class.
09 September Topic: Conditional Probability (Discrete) [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class. Due: HW1: Vectorization (by midnight the night before) Released: HW2: Directed Graphical Models	11 September Topic: Lab	12 September Topic: Joint Probability (Discrete) [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class.
16 September Topic: Lab Due: HW2 Checkpoint. Submit it on gradescope on midnight the night before.	18 September Topic: The Ethics of Data [slides] Pre-Class: Read the "Community Guidelines for Ethics Discussions," under "Goals and Expectations." Complete all readings listed in the corresponding chapter. Answer the corresponding questions to prepare for class discussions.	19 September Topic: Maximum Likelihood: Theory [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class. Due: HW2: Directed Graphical Models (by midnight the night before) Released: HW3: Frequentist Learning
23 September Topic: Maximum Likelihood: Code [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class.	25 September Topic: Lab	26 September Topic: Optimization [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class.
30 September Topic: Probability (Continuous) [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class. Due: Checkpoint 3.1 (by midnight the night before)	02 October Topic: Lab	03 October Topic: The Ethics of Learning from Data [slides] Pre-Class: Complete all readings listed in the corresponding chapter. Answer the corresponding questions to prepare for class discussions. Due: Checkpoint 3.2 (by midnight the night before)
07 October Topic: Regression [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class. Due: HW3: Frequentist Learning (by midnight the night before) Released: HW4: Predictive Models	09 October Topic: Lab	10 October Topic: Classification [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class.
14 October Indigenous Peoples’ Day: no classes.	16 October Topic: Neural Networks [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class. Due: Checkpoint 4.1 (by midnight the night before)	17 October Topic: Lab
21 October Topic: Model Selection & Evaluation [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class.	23 October Topic: Lab	24 October Topic: The Ethics of Predictive Models in Sociotechnical Systems [slides] Pre-Class: Complete all readings listed in the corresponding chapter. Answer the corresponding questions to prepare for class discussions. Due: Checkpoint 4.2 (by midnight the night before)
28 October Topic: Gaussian Mixture Models (Clustering) [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class. Due: HW4: Predictive Models (by midnight the night before) Released: HW5: Generative Models	30 October Topic: Lab	31 October Topic: Factor Analysis (Dimensionality Reduction) [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class.
04 November Topic: Lab Due: Checkpoint 5.1 and 5.3 (by midnight the night before)	06 November Topic: Lab	07 November Topic: Guest Lecture: Dr. Weiwei Pan on the Ethics of Generative Models in Sociotechnical Systems Pre-Class: Complete all readings listed in the corresponding chapter. Answer the corresponding questions to prepare for class discussions.
11 November Topic: Bayesian Models: Priors & Posteriors [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class. Due: HW5: Generative Models (by midnight the night before) Released: HW6: Bayesian Models Extra-Credit: Attend the talk by Dr. Safiya Noble, Algorithms of Oppression Talk is at 5-6:30pm @ Tishman Commons Ethics discussion from 5-6:30pm @ SCI H305	13 November Topic: Lab	14 November Topic: Bayesian Models: Posterior Predictive [slides] Pre-Class: Read corresponding chapter; prepare questions to ask in class.
18 November Topic: Lab Due: Checkpoint 6.1 and 6.3 (by midnight the night before)	20 November Topic: Lab	21 November Topic: The Ethics of Uncertainty and Interpretability in Human-AI Systems [slides] Pre-Class: Complete all readings listed in the corresponding chapter. Answer the corresponding questions to prepare for class discussions.
25 November Topic: Guest Lecture: Prof. Emma Pierson, who develops ML methods to study inequality and healthcare. Due: HW6: Bayesian Models (by midnight the night before) Released: HW7: The Ethics of Machine Learning	27 November Thanksgiving Break: no classes.	28 November Thanksgiving Break: no classes.
02 December Topic: Variational Autoencoders (VAEs) Due: Checkpoint 7.1 (by midnight the night before)	04 December Topic: Neural Ordinary Differential Equations for Irregularly-Sampled Time Series	05 December Topic: The Ethics of Machine Learning: A View from History (Part 1) [slides] Pre-Class: Complete all readings listed in the corresponding chapter. Answer the corresponding questions to prepare for class discussions. Due: Checkpoint 7.2 (by midnight the night before)
09 December Topic: The Ethics of Machine Learning: A View from History (Part 2) [slides] Pre-Class: Complete all readings listed in the corresponding chapter. Answer the corresponding questions to prepare for class discussions. Due: HW7: The Ethics of Machine Learning (by midnight the night before)	11 December Topic: Group Reflection [slides]	12 December Reading Period Begins.
16 December	18 December	19 December

Acknowledgements: The schedule design and CSS is based on Fall 2018’s CS240’s schedule.