Value Function Methods. CS : Deep Reinforcement Learning Sergey Levine
|
|
|
- May Payne
- 5 years ago
- Views:
Transcription
1 Value Function Methods CS : Deep Reinforcement Learning Sergey Levine
2 Class Notes 1. Homework 2 is due in one week 2. Remember to start forming final project groups and writing your proposal! Proposal due 9/26, in two weeks Submission instructions coming soon
3 Today s Lecture 1. What if we just use a critic, without an actor? 2. Extracting a policy from a value function 3. The Q-learning algorithm 4. Extensions: continuous actions, improvements Goals: Understand how value functions give rise to policies Understand the Q-learning algorithm Understand practical considerations for Q-learning
4 Recap: actor-critic fit a model to estimate return generate samples (i.e. run the policy) improve the policy
5 Can we omit policy gradient completely? forget policies, let s just do this! fit a model to estimate return generate samples (i.e. run the policy) improve the policy
6 Policy iteration High level idea: fit a model to estimate return how to do this? generate samples (i.e. run the policy) improve the policy
7 Dynamic programming just use the current estimate here
8 Policy iteration with dynamic programming fit a model to estimate return generate samples (i.e. run the policy) improve the policy
9 Even simpler dynamic programming approximates the new value! fit a model to estimate return generate samples (i.e. run the policy) improve the policy
10 Fitted value iteration curse of dimensionality fit a model to estimate return generate samples (i.e. run the policy) improve the policy
11 What if we don t know the transition dynamics? need to know outcomes for different actions! Back to policy iteration can fit this using samples
12 Can we do the max trick again? forget policy, compute value directly can we do this with Q-values also, without knowing the transitions? doesn t require simulation of actions! + works even for off-policy samples (unlike actor-critic) + only one network, no high-variance policy gradient - no convergence guarantees for non-linear function approximation (more on this later)
13 Fitted Q-iteration
14 Why is this algorithm off-policy? dataset of transitions Fitted Q-iteration
15 What is fitted Q-iteration optimizing? most guarantees are lost when we leave the tabular case (e.g., when we use neural network function approximation)
16 Online Q-learning algorithms fit a model to estimate return generate samples (i.e. run the policy) improve the policy off policy, so many choices here!
17 Exploration with Q-learning final policy: why is this a bad idea for step 1? epsilon-greedy Boltzmann exploration We ll discuss exploration in more detail in a later lecture!
18 Review Value-based methods Don t learn a policy explicitly Just learn value or Q-function If we have value function, we have a policy Fitted Q-iteration Batch mode, off-policy method Q-learning Online analogue of fitted Q- iteration generate samples (i.e. run the policy) fit a model to estimate return improve the policy
19 Break
20 Value function learning theory
21 Value function learning theory
22 Non-tabular value function learning
23 Non-tabular value function learning Conclusions: value iteration converges (tabular case) fitted value iteration does not converge not in general often not in practice
24 What about fitted Q-iteration? Applies also to online Q-learning
25 But it s just regression! Q-learning is not gradient descent! no gradient through target value
26 A sad corollary An aside regarding terminology
27 Review Value iteration theory Linear operator for backup Linear operator for projection Backup is contraction Value iteration converges Convergence with function approximation Projection is also a contraction Projection + backup is not a contraction Fitted value iteration does not in general converge Implications for Q-learning Q-learning, fitted Q-iteration, etc. does not converge with function approximation But we can make it work in practice! Sometimes tune in next time generate samples (i.e. run the policy) fit a model to estimate return improve the policy
Least Mean Squares Regression
Least Mean Squares Regression Machine Learning Spring 2018 The slides are mainly from Vivek Srikumar 1 Lecture Overview Linear classifiers What functions do linear classifiers express? Least Squares Method
Actor-critic methods. Dialogue Systems Group, Cambridge University Engineering Department. February 21, 2017
Actor-critic methods Milica Gašić Dialogue Systems Group, Cambridge University Engineering Department February 21, 2017 1 / 21 In this lecture... The actor-critic architecture Least-Squares Policy Iteration
Introduction to Reinforcement Learning. CMPT 882 Mar. 18
Introduction to Reinforcement Learning CMPT 882 Mar. 18 Outline for the week Basic ideas in RL Value functions and value iteration Policy evaluation and policy improvement Model-free RL Monte-Carlo and
Review: TD-Learning. TD (SARSA) Learning for Q-values. Bellman Equations for Q-values. P (s, a, s )[R(s, a, s )+ Q (s, (s ))]
![Review: TD-Learning. TD (SARSA) Learning for Q-values. Bellman Equations for Q-values. P (s, a, s )[R(s, a, s )+ Q (s, (s ))]](/thumbs/94/119187228.jpg "Review: TD-Learning. TD (SARSA) Learning for Q-values. Bellman Equations for Q-values. P (s, a, s )[R(s, a, s )+ Q (s, (s ))]")
Review: TD-Learning function TD-Learning(mdp) returns a policy Class #: Reinforcement Learning, II 8s S, U(s) =0 set start-state s s 0 choose action a, using -greedy policy based on U(s) U(s) U(s)+ [r
CS599 Lecture 2 Function Approximation in RL
CS599 Lecture 2 Function Approximation in RL Look at how experience with a limited part of the state set be used to produce good behavior over a much larger part. Overview of function approximation (FA)
Least Mean Squares Regression. Machine Learning Fall 2018
Least Mean Squares Regression Machine Learning Fall 2018 1 Where are we? Least Squares Method for regression Examples The LMS objective Gradient descent Incremental/stochastic gradient descent Exercises
CSC321 Lecture 8: Optimization
CSC321 Lecture 8: Optimization Roger Grosse Roger Grosse CSC321 Lecture 8: Optimization 1 / 26 Overview We ve talked a lot about how to compute gradients. What do we actually do with them? Today s lecture:
CSE 190: Reinforcement Learning: An Introduction. Chapter 8: Generalization and Function Approximation. Pop Quiz: What Function Are We Approximating?
CSE 190: Reinforcement Learning: An Introduction Chapter 8: Generalization and Function Approximation Objectives of this chapter: Look at how experience with a limited part of the state set be used to
CSC321 Lecture 22: Q-Learning
CSC321 Lecture 22: Q-Learning Roger Grosse Roger Grosse CSC321 Lecture 22: Q-Learning 1 / 21 Overview Second of 3 lectures on reinforcement learning Last time: policy gradient (e.g. REINFORCE) Optimize
Week 5: Logistic Regression & Neural Networks
Week 5: Logistic Regression & Neural Networks Instructor: Sergey Levine 1 Summary: Logistic Regression In the previous lecture, we covered logistic regression. To recap, logistic regression models and
Reinforcement Learning Part 2
Reinforcement Learning Part 2 Dipendra Misra Cornell University dkm@cs.cornell.edu https://dipendramisra.wordpress.com/ From previous tutorial Reinforcement Learning Exploration No supervision Agent-Reward-Environment
Reinforcement Learning
Reinforcement Learning Ron Parr CompSci 7 Department of Computer Science Duke University With thanks to Kris Hauser for some content RL Highlights Everybody likes to learn from experience Use ML techniques
Simple Techniques for Improving SGD. CS6787 Lecture 2 Fall 2017
Simple Techniques for Improving SGD CS6787 Lecture 2 Fall 2017 Step Sizes and Convergence Where we left off Stochastic gradient descent x t+1 = x t rf(x t ; yĩt ) Much faster per iteration than gradient
Reinforcement Learning
Reinforcement Learning Function approximation Mario Martin CS-UPC May 18, 2018 Mario Martin (CS-UPC) Reinforcement Learning May 18, 2018 / 65 Recap Algorithms: MonteCarlo methods for Policy Evaluation
CS230: Lecture 9 Deep Reinforcement Learning
CS230: Lecture 9 Deep Reinforcement Learning Kian Katanforoosh Menti code: 21 90 15 Today s outline I. Motivation II. Recycling is good: an introduction to RL III. Deep Q-Learning IV. Application of Deep
CSC321 Lecture 7: Optimization
CSC321 Lecture 7: Optimization Roger Grosse Roger Grosse CSC321 Lecture 7: Optimization 1 / 25 Overview We ve talked a lot about how to compute gradients. What do we actually do with them? Today s lecture:
Solving Regression. Jordan Boyd-Graber. University of Colorado Boulder LECTURE 12. Slides adapted from Matt Nedrich and Trevor Hastie
Solving Regression Jordan Boyd-Graber University of Colorado Boulder LECTURE 12 Slides adapted from Matt Nedrich and Trevor Hastie Jordan Boyd-Graber Boulder Solving Regression 1 of 17 Roadmap We talked
Lecture 3: Policy Evaluation Without Knowing How the World Works / Model Free Policy Evaluation
Lecture 3: Policy Evaluation Without Knowing How the World Works / Model Free Policy Evaluation CS234: RL Emma Brunskill Winter 2018 Material builds on structure from David SIlver s Lecture 4: Model-Free
Machine Learning Basics Lecture 3: Perceptron. Princeton University COS 495 Instructor: Yingyu Liang
Machine Learning Basics Lecture 3: Perceptron Princeton University COS 495 Instructor: Yingyu Liang Perceptron Overview Previous lectures: (Principle for loss function) MLE to derive loss Example: linear
Markov Decision Processes and Solving Finite Problems. February 8, 2017
Markov Decision Processes and Solving Finite Problems February 8, 2017 Overview of Upcoming Lectures Feb 8: Markov decision processes, value iteration, policy iteration Feb 13: Policy gradients Feb 15:
CSE 190: Reinforcement Learning: An Introduction. Chapter 8: Generalization and Function Approximation. Pop Quiz: What Function Are We Approximating?
CSE 190: Reinforcement Learning: An Introduction Chapter 8: Generalization and Function Approximation Objectives of this chapter: Look at how experience with a limited part of the state set be used to
CS545 Contents XVI. l Adaptive Control. l Reading Assignment for Next Class
CS545 Contents XVI Adaptive Control Model Reference Adaptive Control Self-Tuning Regulators Linear Regression Recursive Least Squares Gradient Descent Feedback-Error Learning Reading Assignment for Next
Lecture 10 - Planning under Uncertainty (III)
Lecture 10 - Planning under Uncertainty (III) Jesse Hoey School of Computer Science University of Waterloo March 27, 2018 Readings: Poole & Mackworth (2nd ed.)chapter 12.1,12.3-12.9 1/ 34 Reinforcement
1 Introduction 2. 4 Q-Learning The Q-value The Temporal Difference The whole Q-Learning process... 5
Table of contents 1 Introduction 2 2 Markov Decision Processes 2 3 Future Cumulative Reward 3 4 Q-Learning 4 4.1 The Q-value.............................................. 4 4.2 The Temporal Difference.......................................
Day 3 Lecture 3. Optimizing deep networks
Day 3 Lecture 3 Optimizing deep networks Convex optimization A function is convex if for all α [0,1]: f(x) Tangent line Examples Quadratics 2-norms Properties Local minimum is global minimum x Gradient
Lecture 9: Policy Gradient II 1
Lecture 9: Policy Gradient II 1 Emma Brunskill CS234 Reinforcement Learning. Winter 2019 Additional reading: Sutton and Barto 2018 Chp. 13 1 With many slides from or derived from David Silver and John
Lecture 16: FTRL and Online Mirror Descent
Lecture 6: FTRL and Online Mirror Descent Akshay Krishnamurthy akshay@cs.umass.edu November, 07 Recap Last time we saw two online learning algorithms. First we saw the Weighted Majority algorithm, which
Reinforcement Learning and Control
CS9 Lecture notes Andrew Ng Part XIII Reinforcement Learning and Control We now begin our study of reinforcement learning and adaptive control. In supervised learning, we saw algorithms that tried to make
ECE521 lecture 4: 19 January Optimization, MLE, regularization
ECE521 lecture 4: 19 January 2017 Optimization, MLE, regularization First four lectures Lectures 1 and 2: Intro to ML Probability review Types of loss functions and algorithms Lecture 3: KNN Convexity
INF 5860 Machine learning for image classification. Lecture 14: Reinforcement learning May 9, 2018
Machine learning for image classification Lecture 14: Reinforcement learning May 9, 2018 Page 3 Outline Motivation Introduction to reinforcement learning (RL) Value function based methods (Q-learning)
Bayesian Deep Learning
Bayesian Deep Learning Mohammad Emtiyaz Khan AIP (RIKEN), Tokyo http://emtiyaz.github.io emtiyaz.khan@riken.jp June 06, 2018 Mohammad Emtiyaz Khan 2018 1 What will you learn? Why is Bayesian inference
ECE276B: Planning & Learning in Robotics Lecture 16: Model-free Control
ECE276B: Planning & Learning in Robotics Lecture 16: Model-free Control Lecturer: Nikolay Atanasov: natanasov@ucsd.edu Teaching Assistants: Tianyu Wang: tiw161@eng.ucsd.edu Yongxi Lu: yol070@eng.ucsd.edu
Stochastic Gradient Descent: The Workhorse of Machine Learning. CS6787 Lecture 1 Fall 2017
Stochastic Gradient Descent: The Workhorse of Machine Learning CS6787 Lecture 1 Fall 2017 Fundamentals of Machine Learning? Machine Learning in Practice this course What s missing in the basic stuff? Efficiency!
CPSC 340: Machine Learning and Data Mining. Stochastic Gradient Fall 2017
CPSC 340: Machine Learning and Data Mining Stochastic Gradient Fall 2017 Assignment 3: Admin Check update thread on Piazza for correct definition of trainndx. This could make your cross-validation code
Chapter 8: Generalization and Function Approximation
Chapter 8: Generalization and Function Approximation Objectives of this chapter: Look at how experience with a limited part of the state set be used to produce good behavior over a much larger part. Overview
LECTURE 5. Introduction to Econometrics. Hypothesis testing
LECTURE 5 Introduction to Econometrics Hypothesis testing October 18, 2016 1 / 26 ON TODAY S LECTURE We are going to discuss how hypotheses about coefficients can be tested in regression models We will
Stochastic Gradient Descent
Stochastic Gradient Descent Machine Learning CSE546 Carlos Guestrin University of Washington October 9, 2013 1 Logistic Regression Logistic function (or Sigmoid): Learn P(Y X) directly Assume a particular
Math Lecture 18 Notes
Math 1010 - Lecture 18 Notes Dylan Zwick Fall 2009 In our last lecture we talked about how we can add, subtract, and multiply polynomials, and we figured out that, basically, if you can add, subtract,
Trust Region Policy Optimization
Trust Region Policy Optimization Yixin Lin Duke University yixin.lin@duke.edu March 28, 2017 Yixin Lin (Duke) TRPO March 28, 2017 1 / 21 Overview 1 Preliminaries Markov Decision Processes Policy iteration
Reinforcement Learning. Machine Learning, Fall 2010
Reinforcement Learning Machine Learning, Fall 2010 1 Administrativia This week: finish RL, most likely start graphical models LA2: due on Thursday LA3: comes out on Thursday TA Office hours: Today 1:30-2:30
Some AI Planning Problems
Course Logistics CS533: Intelligent Agents and Decision Making M, W, F: 1:00 1:50 Instructor: Alan Fern (KEC2071) Office hours: by appointment (see me after class or send email) Emailing me: include CS533
Neural Networks in Structured Prediction. November 17, 2015
Neural Networks in Structured Prediction November 17, 2015 HWs and Paper Last homework is going to be posted soon Neural net NER tagging model This is a new structured model Paper - Thursday after Thanksgiving
Lecture 9: Policy Gradient II (Post lecture) 2
Lecture 9: Policy Gradient II (Post lecture) 2 Emma Brunskill CS234 Reinforcement Learning. Winter 2018 Additional reading: Sutton and Barto 2018 Chp. 13 2 With many slides from or derived from David Silver
The classifier. Theorem. where the min is over all possible classifiers. To calculate the Bayes classifier/bayes risk, we need to know
The Bayes classifier Theorem The classifier satisfies where the min is over all possible classifiers. To calculate the Bayes classifier/bayes risk, we need to know Alternatively, since the maximum it is
The classifier. Linear discriminant analysis (LDA) Example. Challenges for LDA
The Bayes classifier Linear discriminant analysis (LDA) Theorem The classifier satisfies In linear discriminant analysis (LDA), we make the (strong) assumption that where the min is over all possible classifiers.
Bias-Variance Tradeoff
What s learning, revisited Overfitting Generative versus Discriminative Logistic Regression Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University September 19 th, 2007 Bias-Variance Tradeoff
Machine Learning & Data Mining CMS/CS/CNS/EE 155. Lecture 2: Perceptron & Gradient Descent
Machine Learning & Data Mining CMS/CS/CNS/EE 155 Lecture 2: Perceptron & Gradient Descent Announcements Homework 1 is out Due Tuesday Jan 12 th at 2pm Via Moodle Sign up for Moodle & Piazza if you haven
Loss Functions and Optimization. Lecture 3-1
Lecture 3: Loss Functions and Optimization Lecture 3-1 Administrative: Live Questions We ll use Zoom to take questions from remote students live-streaming the lecture Check Piazza for instructions and
CSC321 Lecture 4 The Perceptron Algorithm
CSC321 Lecture 4 The Perceptron Algorithm Roger Grosse and Nitish Srivastava January 17, 2017 Roger Grosse and Nitish Srivastava CSC321 Lecture 4 The Perceptron Algorithm January 17, 2017 1 / 1 Recap:
CS Deep Reinforcement Learning HW2: Policy Gradients due September 19th 2018, 11:59 pm
CS294-112 Deep Reinforcement Learning HW2: Policy Gradients due September 19th 2018, 11:59 pm 1 Introduction The goal of this assignment is to experiment with policy gradient and its variants, including
Chapter 5: Integrals
Chapter 5: Integrals Section 5.5 The Substitution Rule (u-substitution) Sec. 5.5: The Substitution Rule We know how to find the derivative of any combination of functions Sum rule Difference rule Constant
CS545 Contents XVI. Adaptive Control. Reading Assignment for Next Class. u Model Reference Adaptive Control. u Self-Tuning Regulators
CS545 Contents XVI Adaptive Control u Model Reference Adaptive Control u Self-Tuning Regulators u Linear Regression u Recursive Least Squares u Gradient Descent u Feedback-Error Learning Reading Assignment
Stochastic Gradient Descent. CS 584: Big Data Analytics
Stochastic Gradient Descent CS 584: Big Data Analytics Gradient Descent Recap Simplest and extremely popular Main Idea: take a step proportional to the negative of the gradient Easy to implement Each iteration
Lecture 13: Discriminative Sequence Models (MEMM and Struct. Perceptron)
Lecture 13: Discriminative Sequence Models (MEMM and Struct. Perceptron) Intro to NLP, CS585, Fall 2014 http://people.cs.umass.edu/~brenocon/inlp2014/ Brendan O Connor (http://brenocon.com) 1 Models for
CSC411 Fall 2018 Homework 5
Homework 5 Deadline: Wednesday, Nov. 4, at :59pm. Submission: You need to submit two files:. Your solutions to Questions and 2 as a PDF file, hw5_writeup.pdf, through MarkUs. (If you submit answers to
Lecture 2: Linear regression
Lecture 2: Linear regression Roger Grosse 1 Introduction Let s ump right in and look at our first machine learning algorithm, linear regression. In regression, we are interested in predicting a scalar-valued
Deep Reinforcement Learning: Policy Gradients and Q-Learning
Deep Reinforcement Learning: Policy Gradients and Q-Learning John Schulman Bay Area Deep Learning School September 24, 2016 Introduction and Overview Aim of This Talk What is deep RL, and should I use
Introduction to Machine Learning Prof. Sudeshna Sarkar Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur
Introduction to Machine Learning Prof. Sudeshna Sarkar Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Module 2 Lecture 05 Linear Regression Good morning, welcome
Classification with Perceptrons. Reading:
Classification with Perceptrons Reading: Chapters 1-3 of Michael Nielsen's online book on neural networks covers the basics of perceptrons and multilayer neural networks We will cover material in Chapters
Structure Learning: the good, the bad, the ugly
Readings: K&F: 15.1, 15.2, 15.3, 15.4, 15.5 Structure Learning: the good, the bad, the ugly Graphical Models 10708 Carlos Guestrin Carnegie Mellon University September 29 th, 2006 1 Understanding the uniform
CS260: Machine Learning Algorithms
CS260: Machine Learning Algorithms Lecture 4: Stochastic Gradient Descent Cho-Jui Hsieh UCLA Jan 16, 2019 Large-scale Problems Machine learning: usually minimizing the training loss min w { 1 N min w {
Comparison of Modern Stochastic Optimization Algorithms
Comparison of Modern Stochastic Optimization Algorithms George Papamakarios December 214 Abstract Gradient-based optimization methods are popular in machine learning applications. In large-scale problems,
Lecture 4: Approximate dynamic programming
IEOR 800: Reinforcement learning By Shipra Agrawal Lecture 4: Approximate dynamic programming Deep Q Networks discussed in the last lecture are an instance of approximate dynamic programming. These are
Least squares temporal difference learning
Least squares temporal difference learning TD(λ) Good properties of TD Easy to implement, traces achieve the forward view Linear complexity = fast Combines easily with linear function approximation Outperforms
Learning from Data: Regression
November 3, 2005 http://www.anc.ed.ac.uk/ amos/lfd/ Classification or Regression? Classification: want to learn a discrete target variable. Regression: want to learn a continuous target variable. Linear
Algorithms other than SGD. CS6787 Lecture 10 Fall 2017
Algorithms other than SGD CS6787 Lecture 10 Fall 2017 Machine learning is not just SGD Once a model is trained, we need to use it to classify new examples This inference task is not computed with SGD There
Latent Variable Models
Latent Variable Models Stefano Ermon, Aditya Grover Stanford University Lecture 5 Stefano Ermon, Aditya Grover (AI Lab) Deep Generative Models Lecture 5 1 / 31 Recap of last lecture 1 Autoregressive models:
Comments. Assignment 3 code released. Thought questions 3 due this week. Mini-project: hopefully you have started. implement classification algorithms
Neural networks Comments Assignment 3 code released implement classification algorithms use kernels for census dataset Thought questions 3 due this week Mini-project: hopefully you have started 2 Example:
Q-learning. Tambet Matiisen
Q-learning Tambet Matiisen (based on chapter 11.3 of online book Artificial Intelligence, foundations of computational agents by David Poole and Alan Mackworth) Stochastic gradient descent Experience
Introduction to Machine Learning HW6
CS 189 Spring 2018 Introduction to Machine Learning HW6 Your self-grade URL is http://eecs189.org/self_grade?question_ids=1_1,1_ 2,2_1,2_2,3_1,3_2,3_3,4_1,4_2,4_3,4_4,4_5,4_6,5_1,5_2,6. This homework is
CS 287: Advanced Robotics Fall Lecture 14: Reinforcement Learning with Function Approximation and TD Gammon case study
CS 287: Advanced Robotics Fall 2009 Lecture 14: Reinforcement Learning with Function Approximation and TD Gammon case study Pieter Abbeel UC Berkeley EECS Assignment #1 Roll-out: nice example paper: X.
Reinforcement Learning
Reinforcement Learning Cyber Rodent Project Some slides from: David Silver, Radford Neal CSC411: Machine Learning and Data Mining, Winter 2017 Michael Guerzhoy 1 Reinforcement Learning Supervised learning:
1 Review and Overview
DRAFT a final version will be posted shortly CS229T/STATS231: Statistical Learning Theory Lecturer: Tengyu Ma Lecture # 16 Scribe: Chris Cundy, Ananya Kumar November 14, 2018 1 Review and Overview Last
Linear Models for Classification
Linear Models for Classification Oliver Schulte - CMPT 726 Bishop PRML Ch. 4 Classification: Hand-written Digit Recognition CHINE INTELLIGENCE, VOL. 24, NO. 24, APRIL 2002 x i = t i = (0, 0, 0, 1, 0, 0,
Advanced Policy Gradient Methods: Natural Gradient, TRPO, and More. March 8, 2017
Advanced Policy Gradient Methods: Natural Gradient, TRPO, and More March 8, 2017 Defining a Loss Function for RL Let η(π) denote the expected return of π [ ] η(π) = E s0 ρ 0,a t π( s t) γ t r t We collect
A Gentle Introduction to Reinforcement Learning
A Gentle Introduction to Reinforcement Learning Alexander Jung 2018 1 Introduction and Motivation Consider the cleaning robot Rumba which has to clean the office room B329. In order to keep things simple,
Non-Convex Optimization. CS6787 Lecture 7 Fall 2017
Non-Convex Optimization CS6787 Lecture 7 Fall 2017 First some words about grading I sent out a bunch of grades on the course management system Everyone should have all their grades in Not including paper
Overfitting, Bias / Variance Analysis
Overfitting, Bias / Variance Analysis Professor Ameet Talwalkar Professor Ameet Talwalkar CS260 Machine Learning Algorithms February 8, 207 / 40 Outline Administration 2 Review of last lecture 3 Basic
CSC321 Lecture 2: Linear Regression
CSC32 Lecture 2: Linear Regression Roger Grosse Roger Grosse CSC32 Lecture 2: Linear Regression / 26 Overview First learning algorithm of the course: linear regression Task: predict scalar-valued targets,
CS168: The Modern Algorithmic Toolbox Lecture #6: Regularization
CS168: The Modern Algorithmic Toolbox Lecture #6: Regularization Tim Roughgarden & Gregory Valiant April 18, 2018 1 The Context and Intuition behind Regularization Given a dataset, and some class of models
Generative v. Discriminative classifiers Intuition
Logistic Regression (Continued) Generative v. Discriminative Decision rees Machine Learning 10701/15781 Carlos Guestrin Carnegie Mellon University January 31 st, 2007 2005-2007 Carlos Guestrin 1 Generative
Reinforcement Learning. Yishay Mansour Tel-Aviv University
Reinforcement Learning Yishay Mansour Tel-Aviv University 1 Reinforcement Learning: Course Information Classes: Wednesday Lecture 10-13 Yishay Mansour Recitations:14-15/15-16 Eliya Nachmani Adam Polyak
CPSC 340: Machine Learning and Data Mining. Regularization Fall 2017
CPSC 340: Machine Learning and Data Mining Regularization Fall 2017 Assignment 2 Admin 2 late days to hand in tonight, answers posted tomorrow morning. Extra office hours Thursday at 4pm (ICICS 246). Midterm
Reinforcement Learning. Spring 2018 Defining MDPs, Planning
Reinforcement Learning Spring 2018 Defining MDPs, Planning understandability 0 Slide 10 time You are here Markov Process Where you will go depends only on where you are Markov Process: Information state
Stat Lecture 20. Last class we introduced the covariance and correlation between two jointly distributed random variables.
Stat 260 - Lecture 20 Recap of Last Class Last class we introduced the covariance and correlation between two jointly distributed random variables. Today: We will introduce the idea of a statistic and
Week 3: Linear Regression
Week 3: Linear Regression Instructor: Sergey Levine Recap In the previous lecture we saw how linear regression can solve the following problem: given a dataset D = {(x, y ),..., (x N, y N )}, learn to
CSC321 Lecture 15: Exploding and Vanishing Gradients
CSC321 Lecture 15: Exploding and Vanishing Gradients Roger Grosse Roger Grosse CSC321 Lecture 15: Exploding and Vanishing Gradients 1 / 23 Overview Yesterday, we saw how to compute the gradient descent
Machine Learning (CSE 446): Multi-Class Classification; Kernel Methods
Machine Learning (CSE 446): Multi-Class Classification; Kernel Methods Sham M Kakade c 2018 University of Washington cse446-staff@cs.washington.edu 1 / 12 Announcements HW3 due date as posted. make sure
Lecture 6. Notes on Linear Algebra. Perceptron
Lecture 6. Notes on Linear Algebra. Perceptron COMP90051 Statistical Machine Learning Semester 2, 2017 Lecturer: Andrey Kan Copyright: University of Melbourne This lecture Notes on linear algebra Vectors
The perceptron learning algorithm is one of the first procedures proposed for learning in neural network models and is mostly credited to Rosenblatt.
1 The perceptron learning algorithm is one of the first procedures proposed for learning in neural network models and is mostly credited to Rosenblatt. The algorithm applies only to single layer models
Logistic Regression Introduction to Machine Learning. Matt Gormley Lecture 8 Feb. 12, 2018
10-601 Introduction to Machine Learning Machine Learning Department School of Computer Science Carnegie Mellon University Logistic Regression Matt Gormley Lecture 8 Feb. 12, 2018 1 10-601 Introduction
Algorithms for MDPs and Their Convergence
MS&E338 Reinforcement Learning Lecture 2 - April 4 208 Algorithms for MDPs and Their Convergence Lecturer: Ben Van Roy Scribe: Matthew Creme and Kristen Kessel Bellman operators Recall from last lecture
CPSC 540: Machine Learning
CPSC 540: Machine Learning First-Order Methods, L1-Regularization, Coordinate Descent Winter 2016 Some images from this lecture are taken from Google Image Search. Admin Room: We ll count final numbers
Response Surface Methods
Response Surface Methods 3.12.2014 Goals of Today s Lecture See how a sequence of experiments can be performed to optimize a response variable. Understand the difference between first-order and second-order
Lecture 16: Introduction to Neural Networks
Lecture 16: Introduction to Neural Networs Instructor: Aditya Bhasara Scribe: Philippe David CS 5966/6966: Theory of Machine Learning March 20 th, 2017 Abstract In this lecture, we consider Bacpropagation,
Deep Learning Lab Course 2017 (Deep Learning Practical)
Deep Learning Lab Course 207 (Deep Learning Practical) Labs: (Computer Vision) Thomas Brox, (Robotics) Wolfram Burgard, (Machine Learning) Frank Hutter, (Neurorobotics) Joschka Boedecker University of
Support vector machines Lecture 4
Support vector machines Lecture 4 David Sontag New York University Slides adapted from Luke Zettlemoyer, Vibhav Gogate, and Carlos Guestrin Q: What does the Perceptron mistake bound tell us? Theorem: The
15-889e Policy Search: Gradient Methods Emma Brunskill. All slides from David Silver (with EB adding minor modificafons), unless otherwise noted
15-889e Policy Search: Gradient Methods Emma Brunskill All slides from David Silver (with EB adding minor modificafons), unless otherwise noted Outline 1 Introduction 2 Finite Difference Policy Gradient
High-dimensional regression
High-dimensional regression Advanced Methods for Data Analysis 36-402/36-608) Spring 2014 1 Back to linear regression 1.1 Shortcomings Suppose that we are given outcome measurements y 1,... y n R, and
CSC2541 Lecture 5 Natural Gradient
CSC2541 Lecture 5 Natural Gradient Roger Grosse Roger Grosse CSC2541 Lecture 5 Natural Gradient 1 / 12 Motivation Two classes of optimization procedures used throughout ML (stochastic) gradient descent,