Schedule

Schedule#

Note

We may adapt the course schedule to accomodate your learning!

Monday Wednesday Thursday
19 January
  • Martin Luther King Jr. Day: no classes
21 January 22 January
  • Topic: Introduction to Vectorization + Data Visualization
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
26 January
  • Topic: Probability (Discrete) [slides]
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
28 January
  • Topic: Lab
  • Due: HW1 by 9pm on Gradescope, and this reflection by 10pm
  • Released: HW2
29 January
  • Topic: Conditional Probability (Discrete) [slides]
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
02 February
  • Topic: Lab
04 February
  • Topic: Lab
  • Due: HW2 by 9pm on Gradescope, and this reflection by 10pm
  • Released: HW3
05 February
  • Topic: Joint Probability (Discrete) [slides]
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
09 February
  • Topic: Lab
11 February
  • Topic: Lab
  • Due: HW3 (A+B) by 9pm on Gradescope, and this reflection by 10pm
  • Released: HW4
12 February
  • 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.
16 February
  • President's Day: Monday schedule on Tuesday!
  • Topic: Maximum Likelihood: Theory [slides]
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
18 February
  • Exam: Midterm I
19 February
  • Topic: Lab
23 February
  • Topic: Lab
25 February
  • Topic: Maximum Likelihood: Code [slides]
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
  • Due: HW4 by 9pm on Gradescope, and this reflection by 10pm
  • Released: HW5
26 February
  • Topic: Lab
02 March
  • Topic: Lab
04 March
  • Topic: Optimization [slides]
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
  • Due: HW5 by 9pm on Gradescope, and this reflection by 10pm
  • Released: HW6
05 March
  • Topic: Lab
09 March
  • Topic: Probability (Continuous) [slides]
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
11 March
  • Exam: Midterm II
12 March
  • 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.
16 March
  • Spring Break: no classes
18 March
  • Spring Break: no classes
19 March
  • Spring Break: no classes
23 March
  • Topic: Regression [slides]
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
25 March
  • Topic: Lab
  • Due: HW6 (A+B+C) by 9pm on Gradescope, and this reflection by 10pm
  • Released: HW7
26 March
  • Topic: Lab
30 March
  • Topic: Classification [slides]
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
01 April
  • Topic: Lab
  • Due: HW7 by 9pm on Gradescope, and this reflection by 10pm
  • Released: HW8
02 April
  • Topic: Lab
06 April
  • Topic: Neural Networks [slides]
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
08 April
  • Topic: Lab
  • Due: HW8 by 9pm on Gradescope, and this reflection by 10pm
  • Released: HW9
09 April
  • Topic: Lab
13 April
  • Topic: Model Selection & Evaluation [slides]
  • Pre-Class: Read corresponding chapter; prepare questions to ask in class.
15 April
  • Ruhlman Conference: no classes
  • Due: HW9 by 9pm on Gradescope, and this reflection by 10pm
  • Released: HW10
16 April
  • Topic: Lab
20 April
  • Patriot's Day: no classes
22 April
  • Topic: Lab or Special Topics
  • Due: HW10 (A+B) by 9pm on Gradescope, and this reflection by 10pm
  • Released: HW11
23 April
  • 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.
27 April
  • 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.
29 April
  • 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: HW11 (A+B+C) by 9pm on Gradescope, and this reflection by 10pm
30 April
  • Substitute Day: Monday Schedule
  • Topic: Final Reflection
04 May
    		06 May
      		07 May

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