CMSI 4320 - Cognitive Systems Design

Professor Andrew Forney • Andrew.Forney[at]lmu.edu • Spring 2023

This course has been archived, so some portions may not be accessible any longer!


Welcome to CMSI-4320 1 / 10 / 23

If you're reading this then you've successfully found the course page for CMSI 4320! Check this page frequently for announcements that are relevant to the course, including notes, homework assignments, and practice problems.

First things first, please read through the course syllabus located here (or in the Materials tab above).


Lecture Notes
Date Lecture Subject
Lecture 13-1
4 / 11 / 23
Regression to Regression
A new class of SCMs enters the ring... with a wee bit of stats to propel it!

Lecture 11-2
3 / 30 / 23
Counterfractionals
(That's not a real term, but suits the topic:) Beginnings of probabilistic, practical, and empirical counterfactuals... with practice!

  Lecture Video (13-2)
  Lecture Video (13-1)
  Lecture Video (12-1)
Lecture 10-2
3 / 24 / 23
Imaginationland
SCMs + our first steps intuiting, and then formalizing, the final layer of the causal hierarchy: counterfactuals.

  Lecture Video (11-2)
  Lecture Video (11-1)
Lecture 9-1
3 / 14 / 23
Adjust Cause
Observational vs. experimental data, the models they suggest, the problems they encounter, and the solutions to those problems.

  Lecture Video (10-2)
  Lecture Video (10-1)
Lecture 8-2
3 / 9 / 23
A Cause for Celebration
...because today we ascend beyond animalistic associations and dip our toes into causal modeling!

  Lecture Video (9-2)
  Lecture Video (9-1)
  Lecture Video (8-2)
Lecture 7-2
2 / 23 / 23
Climbing the Causal Ladder
A review of the associational wrung with Bayesian Networks, plus motivations for the next steps!

  Lecture Video (8-1)
  Lecture Video (7-2)
Lecture 6-1
2 / 14 / 23
RL in RL
Some of the bleeding-edge in RL, including deep-Q-learning, inverse-RL, and others!

  Lecture Video (7-1)
  Lecture Video (6-2)
Lecture 5-2
2 / 9 / 23
Potpou-RL-i
A few best practices in RL, including: Sparse vs. Dense Rewards, Optimistic Sampling, and Policy Search!

  Lecture Video (6-1)
Lecture 5-1
2 / 7 / 23
And Now, Our Feature Presentation...
Feature-based Representations and Approximate Q-Learning!

  Lecture Video (5-2)
  Lecture Video (5-1)
Lecture 4-1
1 / 31 / 23
More Q's than A's
Reinforcement learning heats up! Partially-specified MDPs, exponential moving averages, temporal difference learning, and q-learning.

  Lecture Video (4-2)
Lecture 3-2
1 / 24 / 23
The Value of Time
Value-iteration and the solutions it brings to those janky expectimax trees!

  Lecture Video (4-1)
  Lecture Video (3-2)
Lecture 2-2
1 / 19 / 23
Great Expectations
Formalizing traits of MDPs, including discounting, expectimax search, and more!

  Lecture Video (3-1)
Lecture 2-1
1 / 17 / 23
Markov's Back
Tell a friend... Motivation for Markov Decision Processes.

  Lecture Video (2-2)
Lecture 1-2
1 / 12 / 23
Us Animals Had to Start Somewhere
Motivations for Reinforcement Learning, a little gambling, and snapshots of what's to come!

  Lecture Video (2-1)
  Lecture Video (1-2)
Lecture 1-1
1 / 10 / 23
Cogito, ergo... cogito
A preview into the course's topics, with motivating examples to whet our appetites.

  Lecture Video (1-1)


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