CMSI 4320 - Cognitive Systems Design

Professor Andrew Forney • Andrew.Forney[at]lmu.edu • Spring 2024

This course has been archived, so some portions may not be accessible any longer!

Welcome to CMSI-4320 1 / 9 / 24

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

Lecture Notes
Date	Lecture Subject
Lecture 14-1 4 / 16 / 24	Regression to Regression A new class of SCMs enters the ring... with a wee bit of stats to propel it! Lecture Video (14-2)
Lecture 12-2 3 / 30 / 24	Counterfractionals (That's not a real term, but suits the topic:) Beginnings of probabilistic, practical, and empirical counterfactuals... with practice! Lecture Video (14-1) Lecture Video (13-2) Lecture Video (13-1)
Lecture 12-1 4 / 2 / 24	Imaginationland Our first steps intuiting, and then formalizing, the final layer of the causal hierarchy: counterfactuals! Lecture Video (12-2)
Lecture 11-1 3 / 26 / 24	The Awesome Power of the Coin Flip Feel like experimenting? Observational vs. experimental data and their situation on the causal hierarchy. Lecture Video (12-1)
Lecture 9-1 3 / 14 / 24	Adjust Cause I ain't afraid of no ghosts (in a dataset). Unobserved confounding, identifiability, and adjustment. Lecture Video (11-1) Lecture Video (10-2)
Lecture 8-2 3 / 12 / 24	Feeling Functionless? How to still use SCMs when you don't know the structural equations! Causal Bayesian Networks herein. Lecture Video (10-1)
Lecture 8-1 3 / 5 / 24	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 / 22 / 24	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 / 13 / 24	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 / 8 / 24	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 / 6 / 24	And Now, Our Feature Presentation... Feature-based Representations and Approximate Q-Learning! Lecture Video (5-2) Lecture Video (5-1)
Lecture 4-1 1 / 30 / 24	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 / 25 / 24	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 / 24	Great Expectations Formalizing traits of MDPs, including discounting, expectimax search, and more! Lecture Video (3-1)
Lecture 2-1 1 / 16 / 24	Markov's Back Tell a friend... Motivation for Markov Decision Processes. Lecture Video (2-2)
Lecture 1-2 1 / 11 / 24	Us Animals Had to Start Somewhere Motivations for Reinforcement Learning, a little gambling, and snapshots of what's to come! Lecture Video (2-1-2)* Lecture Video (2-1-1)* * Online class while I was sick, split into 2 parts because of S-tier internet.
Lecture 1-1 1 / 9 / 24	Cogito, ergo... cogito A preview into the course's topics, with motivating examples to whet our appetites. Lecture Video (1-2) Lecture Video (1-1)

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Supplementary Class Materials
Date	Item
1 / 9 / 24	Course Syllabus Contains vital information about the class, contact information, dates, homework, policies, and grading.