Computer Science
CSC8030
Introduction to Electrical Engineering and Computer Science I: Unit 4: Probability and Planning

This course contains Unit Four of "Introduction to Electrical Engineering and Computer Science I" from MIT OpenCourseWare.

Introduction to Electrica...

This unit contains information pertaining to "Introduction to Electrical Engineering and Computer Science I."

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Introduction to Electrical Engineering and Compute...

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Software for Introduction to Electrical Engineerin...

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Lab Infrastructure Guide: Introduction to Electric...

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Circuits Maximus Documentation

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Think Python: An Introduction to Software Design

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Python Tutorial Information

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Unit 1: Software Engineering Information

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Unit 2: Signals and Systems Information

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Unit 3: Circuits Information

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Unit 4: Probability and Planning Information

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Introduction to Electrical Engineering and Compute...

Discrete Probability

In this unit, we'll address the problem that systems we design may have to operate under uncertainty, and that we may want those systems to be able to search the world for possible solutions to problems. We'll introduce the basics of probability and search in this session, and apply those concepts to our design challenges.

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Session 10 Handout: Discrete Probability

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Chapter 7: Probabilistic State Estimation

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Lecture 10: Discrete Probability and State Estimat...

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Lecture 10: Discrete Probability and State Estimat...

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Lecture 10: Discrete Probability and State Estimat...

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Lecture 10: Discrete Probability and State Estimat...

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Lecture 10: Discrete Probability and State Estimat...

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Recitation 13: Probability: Basics Transcript

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Recitation 13: Probability: Basics

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Recitation 13: Probability: Basics

Discrete Probability Sess...

This unit contains problems pertaining to "Session 10: Discrete Probability."

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Software Lab 10: Distributions

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Software Lab 10: Distributions Code

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Problem # 10.1.1: Probability Distributions: DDist

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Problem # 10.1.2: Conditional Distributions

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Problem # 10.1.3: Joint Distributions

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Problem # 10.1.4: Operations on Conditional Distri...

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Problem # 10.1.5: Where are you?

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Problem # 10.1.6: Implementing Joint Distributions

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Problem # 10.1.7: Implementing Operations on Condi...

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Design Lab 10: Robot Pets

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Design Lab 10: Robot Pets Code

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Problem # 10.3.1: A Distribution

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Problem # 10.3.2: Summing to 1

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Problem # 10.3.3: Truth

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Problem # 10.3.4: Equivalence

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Problem 10.3.5: Buying a Car

State Estimation

In this session, we focus on state estimation. We'll use state estimation to estimate the location of a robot in a hallway, and use it to localize a robot. Later we'll be able to localize and map at the same time.

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Session 11 Handout: State Estimation

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Chapter 7: Probabilistic State Estimation

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Recitation 14: Probability: State Estimation Trans...

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Recitation 14: Probability: State Estimation

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Recitation 14: Probability: State Estimation

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Check Yourself State Estimation

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Check Yourself State Estimation Solutions

State Estimation Session ...

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Design Lab 11: Robots in Hallways

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Design Lab 11: Robots in Hallways Code

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Problem # 11.1.1: Observation Models

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Problem # 11.1.2: Transition Models

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Problem # 11.1.4: Simulating Hallways

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Problem # 11.1.5: Simulating Hallways: The Noisy- ...

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Problem # 11.1.6: Sonar Hit

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Problem # 11.1.7: Ideal Sonar Readings

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Software Lab 12: I think therefore where am I

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Software Lab 12: I think therefore where am I Code

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Problem # 12.2.1: Stochastic State Machines

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Problem # 12.2.2: Faster State Estimation

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Problem # 12.2.3: Localization

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Homework 4: Distributions

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Homework 4: Distributions Code

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Problem # 11.2.1: Basic Distributions: Square

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Problem # 11.2.2: Basic Distributions: Triangle

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Problem # 11.2.3: Mixture Distribution

Search Algorithms

In this session, we introduce the idea of search. We've designed systems to tackle static problems and modeled the idea of uncertainty, but we still haven't developed a way to approach that uncertainty and engage in decision-making that we can codify into our systems. We can tackle uncertainty through search; if we know the domain of possible solutions to a problem, and know the steps to get from one part of the domain to the other, then we can search the domain until we reach the solution.

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Session 12 Handout: Search Algorithms

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Chapter 8: Long-Term Decision-Making and Search

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Lecture 12: Search Algorithms Handout

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Lecture 12: Search Algorithms

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Lecture 12: Search Algorithms Transcript

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Lecture 12: Search Algorithms

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Lecture 12: Search Algorithms

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Recitation 15: Search: Basics Transcript

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Recitation 15: Search: Basics

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Recitation 15: Search: Basics

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Check Yourself Search Algorithms

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Check Yourself Search Algorithms Solutions

Search Algorithms Session...

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Software Lab 13: Farmer Goat Wolf Cabbage

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Software Lab 13: Farmer Goat Wolf Cabbage Code

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Problem # 13.1.1: Farmer et al.: Machine

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Problem # 13.1.2: Farmer et al.: Search

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Design Lab 13: I Walk the Line

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Design Lab 13: I Walk the Line Code

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Problem # 13.3.1: Compare Searches

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Problem # 13.3.2: Compare Searches

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Problem # 13.3.3: Paths in the Map

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Problem # 13.3.4: Robot on a Grid

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Problem # 13.3.5: Obstacles

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Problem # 13.3.6: Knight Paths on a Chessboard

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Problem # 13.3.7: Two Robots on a Grid

Optimizing a Search

In this session, we focus on improving search. We can systematically use information we have about the state space we're searching, in order to save us time and space.

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Session 13 Handout: Optimizing a Search

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Chapter 8: Long-Term Decision-Making and Search

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Lecture 13: Optimizing a Search Handout

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Lecture 13: Optimizing a Search Slides

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Lecture 13: Optimizing a Search Transcript

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Lecture 13: Optimizing a Search

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Lecture 13: Optimizing a Search

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Recitation 16: Search: Dynamic Programming, Costs ...

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Recitation 15: Search: Basics

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Recitation 16: Search: Dynamic Programming, Costs ...

Optimizing a Search Sessi...

This unit contains problems pertaining to "Session 13: Optimizing a Search."

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Software Lab 14: Plan from Outer Space

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Software Lab 14: Plan from Outer Space Code

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Problem # 14.1.1: Modeling the World

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Problem # 14.1.2: Robot on a Grid Map

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Design Lab 14: I'm the Map!

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Design Lab 14: I'm the Map! Code

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Problem # 14.2.3: Aliasing Instances

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Problem # 14.2.6: Robot Race Results (Optional)

Final Exam

The final exam is comprehensive across all topics in the course, but topics from Unit 4 are weighted more heavily. This page contains exams and solutions from several semesters.

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Final Exam: Spring 2009

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Final Exam: Spring 2009 Solutions

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Final Exam: Fall 2009

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Final Exam: Fall 2009 Solutions

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Final Exam: Spring 2010

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Final Exam: Spring 2010 Solutions

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Final Exam: Fall 2010

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Final Exam: Fall 2010 Solutions

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Final Exam: Spring 2011

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Final Exam: Spring 2011 Solutions