Alice Gao | Poke

Alice Gao @UC_WNnOkr4IW_zSUWIeOeWbA@youtube.com

1.4K subscribers - no pronouns :c

- Assistant Professor, Teaching Stream at the University of

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L23: Mixed Strategy Nash Equilibrium Question Slides 19-20

L23: Mixed Strategy Nash Equilibrium Part 2

L23: Mixed Strategy Nash Equilibrium Part 1

L23: Pareto Optimal Outcomes of the Prisoner's Dilemma Game Question Slide 11

L23: Nash Equilibrium of the Prisoner's Dilemma Game Question Slide 11

L23: Dominant Strategy Equilibrium of the Prisoner's Dilemma Game Question Slide 9

L23: The Prisoner's Dilemma Game

L23: Pareto Optimality

L22: The Nash Equilibrium Question Slide 30

L22: The Nash Equilibrium

L22: Dominant Strategy Equilibrium Question Slide 26

L22: Dominant Strategy Equilibrium Part 3

L22: Dominant Strategy Equilibrium Part 1

L22: Dominant Strategy Equilibrium Question Slide 22

L22: Dominant Strategy Equilibrium Part 2

L22: Applications of Game Theory

L22: Introducing Game Theory

L21: The Q-Learning Algorithm

L21: Temporal Difference Learning

L21: Properties of the Q-Learning Algorithm

L20: Introducing Reinforcement Learning and the Passive ADP Algorithm

L20: Passive ADP Example

L20: Active ADP Example

L19: Value Iteration Examples and Observations

L19: Policy Iteration Example

L19: The Policy Iteration Algorithm

L19: Introducing Policy Iteration

L19: The Value Iteration Algorithm

L19: Introducing the Bellman Equations

L18: Solving for the Optimal Policy Question Slide 8

L18: Solving for the Optimal Policy

L18: Optimal Policies of the Grid World Part 1

L18: Optimal Policies of the Grid World Part 2

L18: Introducing the Grid World

L18: Policy of a Markov Decision Process

L18: Discounted Reward

L18: MDP versus POMDP

L18: Introducing Markov Decision Process

L16: Calculate the Expected Utilities

L16: Making a Decision Using Variable Elimination Algorithm

L16: Constructing the Utility Function

L16: The Probability of an Accident

L16 Defining the Nodes

L16: Introduction to Decision Theory

L6 ML: Intro to Machine Learning

L6 ML: Intro to Supervised Learning Part 1

L7 DT: Examples of Decision Trees

L6 ML: Intro to Supervised Learning Part 2

L7 DT: Components of a DT and Classifying an Example

L7 DT: Grow a Full Tree

L7 DT: No Features Left

L7 DT: No Examples Left

L7 DT: Intro to Entropy

L7 DT: Calculate the Expected Information Gain

L7 DT: A Full Example

L7 DT: Handling Real-Valued Features Part 1

L7 DT: Handling Real-Valued Features Part 2

L7 DT: Dealing with Overfitting

L13 VEA: The Restrict Operation

L15 HMM Smoothing Calculations