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 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
  • 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
  • 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
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.