Learning from the California Experience: Alternative Fuels, - PowerPoint PPT Presentation

Feb 04, 2024 •318 likes •386 views

Learning from the California Experience: Alternative Fuels, Vehicles, and Infrastructure Supported by Deplo loyment of Alternative Vehicle and Fuel T Technologies In Initiative April February ry November Ju July March Fall 2016

Learning from the California Experience: Alternative Fuels, Vehicles, and Infrastructure Supported by
Deplo loyment of Alternative Vehicle and Fuel T Technologies In Initiative April February ry November Ju July March Fall 2016 2015 2015 2016 2016 2016 2016 2017 2017 2017 2017 Part rtic icip ipating DOTs – WA, A, OR, , CA CA, , NC, C, TX TX, CT CT, , NY and VT EV V In Infrastructure Corrid idor AFV AFV Ad Adoptio ion in in Le Learnin ing fr from th the CA Alternativ Alt ive Fu Fuel l NE NE Cle lean Corridors – Tro roy, NY NY ZEV - NE ZE NE De Deve velo lopment – Po Portl tland, Fleets Fle ts – Au Austin in, TX Exp xperie ience Alt Alternativ ive Innovati In tive Fin Finance – OR OR Fu Fuels ls, Ve Vehic icle les, an and Was ashin ington DC DC In Infr frastructu ture – Sacramento, CA Sa
AFV Pooled Fund Website Resource for DOTs and MPOs • Available at http://www.altfueltoolkit.org • AFV Planning Guide • Action-oriented guide to help state and regional transportation agencies navigate the stages of engagement on AFVs • One toolkit for each workshop • Each toolkit contains: • Resource library • Workshop summary report and presentations • Materials tailored for each workshop, such as fact sheets on AFV legislation
Obje jective: highlight best practices and emerging technologies in California related to alternative fuels and infrastructure. Key topics: • Policy drivers in California for alternative fuels • Roles of each transportation and energy planning agency (DOTs, CARB, CEC, AQMD, MPOs) Workshop • Procurement process of alternative fuels for fleets Objective • Alternative fuel infrastructure planning • Electric vehicle consumer research • Sustainable Freight Action Plan Outcomes: create an online toolkit to aid transportation agencies with alternative fuel vehicle and infrastructure deployment, based on lessons learned from California.
Resource Library Suggested California Factsheets or Templates Workshop Toolkit Elements Data Dashboards with Maps

Recommend

Learning is a never-ending process Tasks come and go, but learning is forever Learn more e ff

NeurIPS 2018 Tutorial on Automatic Machine Learning Learning to Learn Learning Learning Learning Learning Learning automl.org/events -> AutoML Tutorial -> Slides Frank Hutter Joaquin Vanschoren Eindhoven University of Technology

1.36k views • 40 slides

What is Action Learning? What is an ALC? 1 What is Action Learning? Learning by doing

What is Action Learning? What is an ALC? 1 What is Action Learning? Learning by doing Process for bringing together a group of people with varied levels of skills and experience (but with influence, passion, etc. to impact an

191 views • 5 slides

Learning From Data Lecture 2 The Perceptron The Learning Setup A Simple Learning Algorithm: PLA

Learning From Data Lecture 2 The Perceptron The Learning Setup A Simple Learning Algorithm: PLA Other Views of Learning Is Learning Feasible: A Puzzle M. Magdon-Ismail CSCI 4100/6100 recap: The Plan 1. What is Learning? 2. Can We do it?

613 views • 25 slides

4. Learning in MAS 4.1. What is learning? One definition: Bower, Hilgard: Theories of learning,

4. Learning in MAS 4.1. What is learning? One definition: Bower, Hilgard: Theories of learning, Prentice-Hall, 1975: Learning refers to the change in a subjects behavior to a given situation brought about by his repeated experiences in

173 views • 15 slides

What are learning outcomes? Learning Outcomes Learning outcomes are statements that specify what

What are learning outcomes? Learning Outcomes Learning outcomes are statements that specify what learners will know or be able to Everybody Wins do as a result of a learning FYE 2007 activity. Dr. Carolyn Hopper Middle Tennessee State

344 views • 11 slides

Machine Learning 11 AI Slides (6e) c Lin Zuoquan@PKU 1998-2020 11 1 11 Machine Learning

Machine Learning 11 AI Slides (6e) c Lin Zuoquan@PKU 1998-2020 11 1 11 Machine Learning 11.1 Learning agents 11.2 Inductive learning 11.3 Deep learning 11.4 Statistical learning 11.5 Reinforcement learning 11.6 Transfer learning

1.7k views • 159 slides

04 FOCUS ON LEARNING STUDENTS REFLECTING ON THEIR LEARNING In this unit we are learning to

workshop 04 FOCUS ON LEARNING STUDENTS REFLECTING ON THEIR LEARNING In this unit we are learning to . discuss the role of reflection as an important part of learning identify strategies that will help students to reflect on their

539 views • 14 slides

The JOY of LEARNING Think of a topic you are passionate about Think about your learning

The JOY of LEARNING Think of a topic you are passionate about Think about your learning process How much time was spent? How did you practice? To experience research-based learning tools: Focus/Diffuse modes of thinking

1.03k views • 18 slides

Complex learning example: curve fitting t = sin(2 x ) + noise t n t 1 y ( x n , w ) 0 1

Artificial Intelligence: Representation and Problem Solving 15-381 April 10, 2007 Introduction to Learning & Decision Trees Learning and Decision Trees to learning aka: regression What is learning? pattern recognition

462 views • 17 slides

Further thoughts How does e-Learning relate to instruction? How does eLearning change

Ideas from group What is the difference between learning and training? What is the difference between learning and education? Where does learning take place? Who learns? What is the role of instruction in learning? Further

800 views • 47 slides

Learning Sciences: Impact on Learning Technologies & Learning Activities Phillip D. Long,

Learning Sciences: Impact on Learning Technologies & Learning Activities Phillip D. Long, Ph.D. 12-May, 2015 Narrative Arc ( 5 2) Conclusion: What do we want? Learning learning |lrniNG | noun the acquisition of knowledge or

653 views • 36 slides

The Learning Tree Workshop: The Learning Tree Workshop: Regulating Sensation Series on Learning

The Learning Tree Workshop: The Learning Tree Workshop: Regulating Sensation Series on Learning Differences, Learning Challenges, and Learning Strengths Transcript by Stanley I. Greenspan M.D. Narration by Brian MacDonald Sensory Modulation

453 views • 16 slides

Foundations of AI Why learning works 1 6 . Statistical Machine Learning Bayesian Learning and

Contents Statistical learning Foundations of AI Why learning works 1 6 . Statistical Machine Learning Bayesian Learning and Why Learning Works W olfram Burgard, Bernhard Nebel, and Andreas Karw ath 10/ 1 10/ 2 Statistical Learning

305 views • 5 slides

Welcome to Welcome to The Learning Tree Workshop Series on Learning Differences, Learning

Welcome to Welcome to The Learning Tree Workshop Series on Learning Differences, Learning Challenges, and Learning Strengths Strengths As a companion to Dr. Greenspans The Learning Tree : g Overcoming Learning Disabilities From the

367 views • 16 slides

Active Learning Passive Learning Active Learning 1. Think 1. Acquisition of knowledge Ability

Active Learning Passive Learning Active Learning 1. Think 1. Acquisition of knowledge Ability 2. Decision making 2. Application of knowledge 3. Explain/communicate 1. Deep learning Approach to Uni-directional 2. Interactive Learning

282 views • 5 slides

Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

GCT634/AI613: Musical Applications of Machine Learning (Fall 2020) Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning Pipeline in Classification Tasks A set of hand-designed audio features are selected

398 views • 26 slides

1 Why Study Machine Learning? Why Study Machine Learning? Cognitive Science The Time is Ripe

What is Learning? Herbert Simon: Learning is any process by which a system improves performance from CS 391L: Machine Learning experience. Introduction What is the task? Classification Problem solving / planning /

603 views • 6 slides

Meta Learning Shengchao Liu Background Meta Learning (AKA Learning to Learn) A

Meta Learning Shengchao Liu Background Meta Learning (AKA Learning to Learn) A fast-learning algorithm: quickly adapted from the source tasks to the target tasks Key terminologies Support Set & Query Set C-Way K-Shot

2k views • 41 slides

MACHINE LEARNING, STATISTICAL LEARNING AND PARALLEL COMPUTING INTRODUCTION VS MACHINE LEARNING

JACOPO DI IORIO, MATTEO FONTANA, DANIELE OCCHIUTO, CLAUDIA VOLPETTI MACHINE LEARNING, STATISTICAL LEARNING AND PARALLEL COMPUTING INTRODUCTION VS MACHINE LEARNING STATISTICAL LEARNING Separate evolutions, but with shared methodologies MERGE

565 views • 55 slides

Improving Integration of Learning in Learning Communities Through the Development of a Shared

Improving Integration of Learning in Learning Communities Through the Development of a Shared Learning Space Presented by: Emily Richardson richardsone2@queens.edu Director of the Hayworth Center for Online Learning; CATO School of Education

547 views • 15 slides

UNSUPERVISED LEARNING, CLUSTERING UNSUPERVISED LEARNING UNSUPERVISED LEARNING Supervised

UNSUPERVISED LEARNING, CLUSTERING UNSUPERVISED LEARNING UNSUPERVISED LEARNING Supervised learning: X - y pairs, f(x) function approximation Unsupervised learning: only X, no y Exploring the space of X measurements,

440 views • 14 slides

3. Preference Learning Techniques a. Learning Utility Functions b. Learning Preference

AGENDA 1. Preference Learning Tasks 2. Performance Assessment and Loss Functions 3. Preference Learning Techniques a. Learning Utility Functions b. Learning Preference Relations c. Structured Output Prediction d. Model-Based Preference

583 views • 39 slides

Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 12, 2015 Today: Readings: What is machine learning? The Discipline of ML Decision tree learning Mitchell, Chapter 3

872 views • 29 slides

Learning about vs learning to be It's not what you know, It's what you want to learn. How to

Learning about vs learning to be It's not what you know, It's what you want to learn. How to get a job 1. study hard 2. Get good grades 3. Make a resume 4. Do research 5. Apply to jobs 6. Get interviewed 7. Get an offer 8. Accept the

422 views • 26 slides