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Stanford Online: University Courses & Lectures

Stanford Online

You can gain access to a world of education through Stanford Online, the Stanford School of Engineering’s portal for academic and professional education offered by schools and units throughout Stanford University. https://online.stanford.edu/

Our robust catalog of degree programs, credit-bearing education, professional certificate programs, and free and open content is developed by Stanford faculty, enabling you to expand your knowledge, advance your career, and enhance your life.

Stanford Online is operated and managed by the Stanford Engineering Center for Global and Online Education (CGOE). CGOE expands access to Stanford teaching and research, working in collaboration with faculty in the School of Engineering and throughout Stanford University to design and deliver extensive global, online, and enterprise education to a global audience.

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Today · 1 hr 25 min
Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Trajectory Tracking
For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education Lecture 4 — Trajectory Tracking and Closed-Loop Control Note: This lecture was recorded as part of the 2019 edition of the course. Course content, structure, assignments, and other materials may have changed since this recording. The course links below point to the most recent edition and may therefore differ from what is presented in the video. Uploaded 09-10-26 Read the companion textbook, Principles of Robot Autonomy, free online: https://porabook.com Follow along with the latest course schedule and syllabus: https://stanfordasl.github.io/PoRA-I Speaker: Prof. Marco Pavone Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University and a Senior Director of Autonomous Vehicle Research at NVIDIA. Prior to joining Stanford, he was a Research Technologist in the Robotics Section at NASA’s Jet Propulsion Laboratory. Dr. Pavone received his Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His research focuses on the analysis, design, and control of autonomous systems, with particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers from President Barack Obama, the Office of Naval Research Young Investigator Award, the NSF CAREER Award, the NASA Early Career Faculty Award, the IEEE Kiyo Tomiyasu Award, the CSS Award for Technical Excellence in Aerospace Control, and the Early-Career Spotlight Award from the Robotics Science and Systems Foundation. Instructor: Prof. Marco Pavone, Associate Professor of Aeronautics and Astronautics, Director of Autonomous Vehicle Research at NVIDIA Full playlist: https://www.youtube.com/playlist?list=PLR9iNyexfIkk #StanfordOnline #RobotAutonomy #Robotics #PhysicalAI Learn more about your ad choices. Visit megaphone.fm/adchoices
Today · 50 min
Stanford Seminar - Emerging Trends and Applications of Light Field Displays
Gordon Wetzstein Stanford University This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss a wide range of current and evolving topics pertaining to HCI. Uploaded 14-10-15 Learn more about Stanford's Human-Computer Interaction Group: https://hci.stanford.edu Learn about Stanford's Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 10 min
A Conversation About Energy, Ricardo Levy and Howie Rosen
Stanford's Ricardo Levy and Howie Rosen offer insights into entrepreneurial opportunities in the renewable energy industry. What does the future hold? Where will there be innovation? Levy and Rosen teach courses in the new Molecular Engineering of Energy Technologies online graduate certificate at Stanford University. Uploaded 07-08-12 Learn more about the Molecular Engineering of Energy Technologies certificate: http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=15533128 Enroll in graduate courses today! https://online.stanford.edu/explore?type=All&credentials[156]=156 Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 26 min
Stanford CS234 Reinforcement Learning I Offline RL 3 I 2024 I Lecture 10
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Uploaded 30-10-24 To follow along with the course, visit the course website: https://web.stanford.edu/class/cs234/ Emma Brunskill Associate Professor of Computer Science at Stanford University https://cs.stanford.edu/people/ebrun/ Learn more about the course and how to enroll: https://online.stanford.edu/courses/cs234-reinforcement-learning To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 16 min
Stanford CS329A Self-Improving AI Agents | Part 1 | Course Overview
Want to dive deeper? This curriculum is covered in the following online courses: - Agentic AI professional education course: https://stanford.io/4zOyjPN - XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents Uploaded 03-08-26 A similar curriculum is covered in XCS329z https://online.stanford.edu/courses/cs329z-engineering-ai-agents Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/ View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA Video Summary: This first lecture videoof Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery and Azalia Mirhoseini on September 22, 2025, opens with an overview of scaling laws that link model parameters, training compute, and dataset size to lower test loss in large language models from GPT-2 through GPT-4. It covers few-shot and zero-shot learning, the emergence of chain-of-thought reasoning in larger models, and the role of instruction tuning and reinforcement learning from human feedback in the development of ChatGPT. The lecture introduces inference-time scaling through the Large Language Monkeys project, which repeatedly samples a model's outputs and selects correct answers with a verifier to improve performance without retraining. It then traces the shift from single-turn chatbots to agent workflows such as prompt chaining, routing, parallelization, and orchestrator-worker patterns, using Claude Code and deep research tools as examples. The session closes with logistics for the course. Speaker Bios: Aakanksha Chowdhery Adjunct Professor of Computer Science, Stanford University Dr. Aakanksha Chowdhery is pushing the frontier of agentic LLMs, focusing on recursive self-improvement and long-horizon agents that learn and deploy in the real world. She is one of the few researchers globally who has led frontier model training end-to-end, across both dense and mixture-of-experts (MoE) architectures. At Google, she led the 540B PaLM model, the largest densely trained language model in the world at the time. She subsequently drove pre-training and scaling of Gemini's MoE models across multiple generations, and contributed key components to PaLM-E, Med-PaLM, and the Pathways infrastructure underpinning Google's large-model efforts. She went on to build and lead pretraining teams for open intelligence efforts at Reflection and Meta. Earlier, she held research roles at Microsoft Research and Princeton. At Stanford, where she earned her PhD, she teaches CS329A (Self-Improving AI Agents) and serves as Program Chair for MLSys 2026. Azalia Mirhoseini Assistant Professor of Computer Science, Stanford University Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and co Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 11 min
Stanford Seminar - Electric Vehicles and Startups, Marc Tarpenning Co-Founder of Tesla
"Electric Vehicles and Startups" -Marc Tarpenning, Tesla The purpose of this seminar series is to help students and professionals develop the tools to apply the engineering mindset to problems that stem from climate change, in order to consider and evaluate possible stabilizing, remedial and adaptive approaches. This course is not a crash course on climate change or policy. Instead we will focus on learning about and discussing climate problems that seem most likely to benefit from the engineering mindset. Uploaded 14-10-15 Learn more: http://stanford.io/19O05eN Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 25 min
Stanford CS234 I Guest Lecture on DPO: Rafael Rafailov, Archit Sharma, Eric Mitchell I Lecture 9
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Stanford CS234 Reinforcement Learning I Offline RL 2 and Guest Lecture on Direct Preference Optimization (DPO) by authors: Rafael Rafailov: https://arxiv.org/search/cs?searchtype=author&query=Rafailov,+R Archit Sharma: https://arxiv.org/search/cs?searchtype=author&query=Sharma,+A Eric Mitchell: https://arxiv.org/search/cs?searchtype=author&query=Mitchell,+E Uploaded 30-10-24 To follow along with the course, visit the course website: https://web.stanford.edu/class/cs234/ Emma Brunskill Associate Professor of Computer Science at Stanford University https://cs.stanford.edu/people/ebrun/ Learn more about the course and how to enroll: https://online.stanford.edu/courses/cs234-reinforcement-learning To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 55 min
Stanford Seminar - Christina Smolke of Stanford, Steve Jurvetson of DFJ and Astro Teller of Google X
Christina Smolke, Stanford University Steve Jurvetson, DFJ Astro Teller, Google X In this seminar, entrepreneurial leaders share lessons from real-world experiences across entrepreneurial settings. Speakers include entrepreneurs, leaders from global technology companies, venture capitalists, and best-selling authors. Half-hour talks are followed by a half hour of class interaction. Uploaded 14-10-15 Learn more about Stanford's eCorner: https://ecorner.stanford.edu Learn more about Stanford's online classes and certificates: https://online.stanford.edu/programs See the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rPE-kcGtKBgYtSMqEJKbESC Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 13 min
Stanford Robotics Seminar ENGR319 | Winter 2025 | Embodied Intelligence
For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education March 7, 2025 This seminar covers: • Embodied intelligence with morphing materials and mechanisms • Facing open design challenges, such as complete degradability and biocompatibility, arbitrary shape morphing, and freely reconfigurable degrees of freedom • How addressing these challenges sometimes requires looking beyond conventional mechatronic systems • The critical role of computational design and optimization in assisting robotic and machine design and control Uploaded 22-07-26 Follow along with the seminar schedule: https://stanfordasl.github.io/robotics_seminar/archive/ Lining Yao is an Assistant Professor at the Mechanical Engineering department, the University of California, Berkeley, where she directs the Morphing Matter Lab (morphingmatter.org). Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 50 min
Stanford Webinar - Build an Entrepreneurial Culture within your Organization
Entrepreneurship is synonymous with innovation, especially in Silicon Valley. In this webinar Stanford Professors Howie Rosen and Ricardo Levy discuss how your organization can continue to be disruptive in industry and maintain an entrepreneurial edge Uploaded 14-08-13 Learn more about the Molecular Engineering of Energy Technologies graduate certificate: http://scpd.stanford.edu/public/category/courseCategoryCertificateProfile.do?method=load&certificateId=15533128 0:00 Introduction 0:56 Caveat... 1:37 Start-up or established company: evaluating a new business before you "leap" has advantages. 2:23 Successful new businesses have two parts: 1. A "good" idea; and, 2. A "business model" 3:40 Engineering and science-based businesses take time and money to show technical feasibility. 5:59 Learning objectives for today's webinar 6:34 Entrepreneurial leadership 7:23 Key issues 7:40 the difference between leadership and management 9:29 LEADERSHIP vs MANAGEMENT 10:37 Management and leadership with limited resources 13:01 the nature of the entrepreneurial commitment 14:17 Total emotional engagement -- The Positive 15:41 Total emotional engagement -- the negative 17:13 Consequences of control 18:15 The leader's role in the face of uncertainty 19:55 Absorbing Uncertainty... 21:38 The entrepreneurial "edge" 23:45 The established company "edge" 25:00 some of the corporate challenges 26:55 some of the personal challenges to be aware of 29:20 Some final thoughts... 31:01 and remember... Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 20 min
Stanford CS234 Reinforcement Learning I Offline RL 1 I 2024 I Lecture 8
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Uploaded 30-10-24 To follow along with the course, visit the course website: https://web.stanford.edu/class/cs234/ Emma Brunskill Associate Professor of Computer Science at Stanford University https://cs.stanford.edu/people/ebrun/ Learn more about the course and how to enroll: https://online.stanford.edu/courses/cs234-reinforcement-learning To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 15 min
Stanford Seminar - Wafer-Scale Thermionic Energy Converters?
"Wafer-Scale Thermionic Energy Converters?"- Roger T. Howe of Stanford University Uploaded 15-10-15 About the talk: Thermionic energy converters (TECs) were conceived in 1915, demonstrated in 1939, and were the focus of huge investments during the Cold War by the U.S. and the Soviet Union. A 6 kW thermionic converter, fabricated using precision machining technology, was flown in 1987 by the Soviet Union. TEC research ramped down in the 1990s and the field withered to a few embers by 2005. I will review the simple physics of TECs and what the barriers have been to applying them to direct heat-to-electricity conversion for terrestrial applications. Over the past decade, new materials, processes, and device architectures are being explored at Stanford, the Max Planck Institute, and other research groups around the world. I will provide an update on recent progress at Stanford on thermally isolated, suspended micro cathodes inspired by microbolometer infrared imagers. In order to reach useful efficiencies, both very high thermal isolation between the cathode and the anode and a much lower anode work function are needed. Over the past year, two approaches to fabricating anodes with work functions of 1 eV or less have been demonstrated at Stanford: the electrostatic doping of graphene and the surface photovoltage in a semiconductor anode. Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum. Speaker Abstract and Bio can be found here: http://ee380.stanford.edu/Abstracts/151014.html For more info about the EE380 series: http://ee380.stanford.edu Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week. Learn more: http://bit.ly/WinYX5 0:00 Introduction 4:15 Thermionic Energy Converter 6:29 Thermionics Timeline: The Early Days 10:30 A Cold War Application: Space Power 14:28 Thermionic Converter Design Cathode work function sets hot-side temperature 15:46 Barrier Index: a critical parameter 19:00 Microfabricated Thermionic Converters? 20:57 Standard MEMS Thermal Isolation Design 22:34 A Pixelated Suspended Cathode 25:33 Nano-Gap Heat Transfer 28:43 Testing Thermionic Components 29:21 Mixed Alkali Earth Oxide Coatings Lower the Work Function of Tungsten 30:07 Density Functional Theory (DFT) Simulations of Work Function and Formation Energy 32:06 Computation of Thermionic Emission Current from First Principles using DFT 33:14 Work Functions for Cathodes and Anodes 43:08 Topping Cycles for Fossil-Fuel Power Plants: Themoelectronic Triodes 47:03 A 21st Century Application for TEC: Concentrated Solar Thermal Power 48:28 Photon-Enhanced Thermionic Emission (PETE) Energy Diagram 51:04 Tandem Solar Thermal Cycle 55:48 Spark Thermionics @ Cyclotron Road Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 25 min
Stanford CS234 Reinforcement Learning I Policy Search 3 I 2024 I Lecture 7
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Uploaded 30-10-24 To follow along with the course, visit the course website: https://web.stanford.edu/class/cs234/ Emma Brunskill Associate Professor of Computer Science at Stanford University https://cs.stanford.edu/people/ebrun/ Learn more about the course and how to enroll: https://online.stanford.edu/courses/cs234-reinforcement-learning To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 3 min
Stanford Student Profile | Angela Marsiaj of Staeye, Innovation Masters Series
Angela Marsaiaj of Staeye highlights the importance of learning by doing for a small business owner at the Innovation Masters Series and how the experience made "everything come to life." Uploaded 17-10-13 Learn the problem-solving tools and problem-finding frameworks that lead to innovation and strategic leadership in the Innovation Masters Series: Design Thinking and the Art of Innovation course, pioneered by the Design Group and the d.school at Stanford. Learn more: http://scpd.stanford.edu/design/ Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 10 min
Stanford CS329A Self-Improving AI Agents | Part 2 | Test-Time Compute Scaling
Want to dive deeper? This curriculum is covered in the following online courses: - Agentic AI professional education course: https://stanford.io/4zOyjPN - XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents Uploaded 03-08-26 A similar curriculum is covered in XCS329z https://online.stanford.edu/courses/cs329z-engineering-ai-agents Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/ View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA Video Summary: This lecture recording from Stanford's CS329A, Self-Improving AI Agents, delivered by Azalia Mirhoseini on September 26, 2025, examines test-time compute scaling as a way to improve model performance without additional training. It covers the Large Language Monkeys paper's finding that solve rate follows a power law as the number of parallel samples increases, driven by a long tail of hard problems each model solves only rarely, and it distinguishes majority voting from oracle verification to define the generation-verification gap. The lecture also covers a paper on optimally scaling test-time compute, which compares parallel sampling against sequential revision and introduces outcome and process reward models to guide search over candidate solutions. It closes with the Arkon paper on inference-time architecture search, which combines techniques including fusion, critic, ranker, and unit test generation across multiple models, reporting an average 14.1 percent improvement in pass-at-one accuracy over GPT-4 and Claude 3.5 Sonnet on reasoning, math, and coding tasks. Discussion throughout addresses when pre-training still outperforms added test-time compute for the hardest problems. Speaker Bio: Azalia Mirhoseini Assistant Professor of Computer Science, Stanford University Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including WSJ, NYT, Forbes, MIT Technology Review, IEEE Spectrum, WIRED, and TechCrunch. Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 7 min
Neil Daswani - A Conversation on Stanford, Twitter, and Mobile Security
http://scpd.stanford.edu/computerSecurity/ Neil Daswani, co-founder of Dasient, Inc., a stealth-mode Internet security company, is an instructor in the Stanford Advanced Computer Security certificate program. In the past, Neil has served in a variety of research , development, teaching, and managerial roles at Google, Stanford University, DoCoMo USA Labs, Yodlee, and Bellcore (now Telcordia Technologies). While at Stanford, Neil cofounded the Stanford Center for Professional Development (SCPD) Security Certification Program. Uploaded 14-08-12 Enroll in professional education courses today! https://online.stanford.edu/programs/advanced-cybersecurity-program Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 26 min
Stanford CS234 Reinforcement Learning I Policy Search 2 I 2024 I Lecture 6
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Uploaded 30-10-24 To follow along with the course, visit the course website: https://web.stanford.edu/class/cs234/ Emma Brunskill Associate Professor of Computer Science at Stanford University https://cs.stanford.edu/people/ebrun/ Learn more about the course and how to enroll: https://online.stanford.edu/courses/cs234-reinforcement-learning To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 37 min
Stanford Seminar - Mostly Missless Memory in the Mill CPU
"Mostly missless memory in the Mill CPU: Avoiding the pain of cache misses in a statically-scheduled architecture" -Ivan Godard, Out-of-the-Box Computing Uploaded 24-10-13 Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week. Learn more: http://bit.ly/WinYX5 0:00 Introduction 1:02 The Mill Architecture 3:37 Wide issue 5:02 Exposed pipeline 9:16 The memory hierarchy 11:10 The load problem 12:08 Every CPU's goal - hide load latency 13:20 Hardware approach - dynamic scheduling 22:18 Several different load problems... 24:36 Mill "deferred loads" 28:56 Deferred loads vs. alternatives 32:57 Reordering constraints 34:50 So eliminate aliasing 35:53 Alias immunity 37:09 Loads across control flow 38:14 Pickup loads 39:41 Compiler strategy 41:32 The trade-off 42:22 Implementation - the retire station 43:33 Implementation - stream monitoring 46:06 Retire station allocation 48:00 The fine print #1 50:33 The fine print #2 51:29 When stores miss... 52:45 Valid bits 58:50 Hierarchy from 40,000 ft. Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 1 hr 15 min
Stanford CS234 Reinforcement Learning I Policy Search 1 I 2024 I Lecture 5
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Uploaded 30-10-24 To follow along with the course, visit the course website: https://web.stanford.edu/class/cs234/ Emma Brunskill Associate Professor of Computer Science at Stanford University https://cs.stanford.edu/people/ebrun/ Learn more about the course and how to enroll: https://online.stanford.edu/courses/cs234-reinforcement-learning To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu Learn more about your ad choices. Visit megaphone.fm/adchoices
Yesterday · 5 min
Stanford Faculty Profile | Ron Howard, Professor Management Science and Engineering
Professor Howard of the Stanford School of Engineering explains the catalyst for coining the term decision analysis, why he started the Strategic Decision and Risk Management (SDRM) program and how decision analysis can assist anyone with the unanswered, difficult questions in their life. Uploaded 03-09-13 Learn more about the Strategic Decision and Risk Management program and Howard's online Decision Analysis course at: http://strategicdecisions.stanford.edu/ Learn more about your ad choices. Visit megaphone.fm/adchoices

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