Recently Updated Pages
learning theory
Questions Explain what is the VC dimension of a hypothesis space and what it is used for. (6) As ...
linear models for regression
Questions Describe and compare Ridge regression and LASSO algorithms to solve linear regression p...
Introduction
An informed search uses domain-specific hints about the location of goals to find solutions more ...
reinforcement learning
Questions Describe and compare Q-learning and SARSA. (4) Describe the difference between on-polic...
Markov Decision Processes
Questions Describe the properties of the Bellman operators. (1) ... Describe the policy iteration...
list of questions
List of question and appearances as 2023-06-06: Question Appearances Topic Describe and co...
kernel methods
CS480/680 Lecture 11: Kernel Methods Questions from past exams Give the definition of valid k...
bias-variance, model selection and model ensembles
Questions Describe the PCA technique and what it is used for. (3) Principal Component Analysis (P...
linear models for classification
Questions Describe the Perceptron model and how it is trained. (1) The Perceptron algorithm is an...
Bayesian Networks
Bayesian Networks are data structure that represents the dependencies among random categorical va...
Minimax search
MAX wants to find a sequence of actions leading to a win, but MIN has something to say about it. ...
A* search
The most common informed search algorithm is A* search (pronounced “A-star search”), a best-first...
Depth-limited and iterative deepening search
Depth-limited To keep depth-first search from wandering down an infinite path, we can use depth-l...
Depth-first search
Depth-first search always expands the deepest node in the frontier first. It could be implemented...
Breadth-first search
When all actions have the same cost, an appropriate strategy is breadth-first search, in which th...
Example problems on uncertainty
Some example problems that could be asked in the exam: Given some knowledge about a problem, rep...
Introduction to uncertainty
Agents in the real world need to handle uncertainty, whether due to partial observability, nondet...
Uncertainty over time
We consider the Markov assumption: The current state $X_t$ depends on only a finite fixed number ...
Q-learning
We apply a policy to explore the environment in order to collect information and we keep a progre...
Value function and Q-function
The total reward is typically defined s the expected discounted cumulative reward. We can define...