Sunday, September 18, 2022

The Artificial Intelligence Challenge



I took  coursera's Learning How To Learn and became fascinated with the story of a young man who taught himself the content of a 4 year MIT Computer Science curriculum within 12 months.  His name is Scott Young and he published videos at the start and completion of his MIT Challenge:

The MIT Challenge

MIT Challenge Complete

At the end of his challenge Scott identified 3 recommendations for learning on your own:

  1. Identify a goal. This provides a concrete focus
  2. Create a curriculum. This gives you a path for accomplishing your goal.
  3. Announce your intention. This bumps up your accountability.
Lately I've been seeing terms like Artificial Intelligence,  Machine Learning, Deep Learning, and Robotics. It's a simple process to look the terms up and get a glimmer of what they mean. But what if I wanted to get a "college level" understanding of these terms:
  • what would it take to teach myself Artificial Intelligence, Machine Learning, etc.?
  • what's available online? 
Besides being inspired by Scott Young's MIT Challenge, I used MIT online resources to answer my questions because:
  • MIT is highly regarded in math and sciences
  • Beginning in the Fall of 2022 MIT is offering an Artificial Intelligence and Decision Making degree and has published its curriculum
  • MIT generously shares content information through MITOpenCourseWare
  • MIT generously provides instruction through MITOpenLearningLibrary

The resulting Artificial Intelligence Challenge (AIC) table answers my two questions and addresses Scott's first two recommendations if you want to teach yourself "Artificial Intelligence".  Most of the current MIT curriculum courses are not offered online. The online available equivalent course is documented in NOTES and  links in the RESOURCES, TEXTBOOK, and ONLINE columns are associated with this equivalent course.

Granted, not many of us have a need to take the complete AIC, but the table provides a way to map a path to the understanding we seek. For example, if I want to have a "college level" understanding of Machine Learning, 
  1. I'll enroll in MITOpenLibraryLearning's Introduction to Machine Learning (6.39)
  2. The assessment quiz at the start of this course makes me aware that I need to brush up/enroll in Linear Algebra (18.C06)
  3. and Python (6.100A)
My other alternative is to read Machine Learning for Dummies. Hmmm . . . maybe that should be my first step : )


Artificial Inteligence Challenge
NUMBERCATEGORYNAMERESOURCESTEXTBOOKONLINENOTES
Departmental Program
Choose at least two subjects in the major that are designated as communication-intensive (CI-M) to fulfill the Communication Requirement.
6.100AProgamming SkillsIntroduction to Computer Science Programming in PythonSyllabusGuttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data Second Edition. MIT Press, 2016. ISBN: 9780262529624An Introduction to Programming Using Python by David Schneidern in Internet Archives
6.121FoundationIntroduction to AlgorithmsSyllabusIntroduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (Third Edition, MIT Press) ISBN: 9780262033848Data Science Programming in Python by Anita RaichandNOTE: 6.006 found in MIT OCW
6.101FoundationFundamentals of ProgrammingSyllabusGuttag, John. Introduction to Computation and Programming Using Python. Spring 2013 edition. MIT Press, 2013. ISBN: 9780262519632.An Introduction to Programming Using Python by David Schneidern in Internet ArchivesNOTE: 6.00SC found in MIT OCW
6.1200[J]MathMathematics for Computer ScienceSyllabusMathematics for Computer ScienceMIT Online Publication
18.C06MathLinear Algebra and Optimization 1SyllabusStrang, Gilbert. Introduction to Linear Algebra. 5th ed. Wellesley, MA: Wellesley-Cambridge Press , February 2016. ISBN: 9780980232776Strang, Gilbert. Introduction to Linear Algebra. 4th ed.NOTE: 18.06SC found in MIT OCW
Select one of the following:
6.37MathIntroduction to ProbabilityIntroduction to ProbabilityIntroduction to ProbabilityIntroduction to ProbabilityNOTE: RES.6-012 found in MIT OCW
6.38MathIntroduction to InferenceSyllabusOppenheim, Alan, and George Verghese. Signals, Systems and Inference. Pearson, 2017. ISBN: 9781292156200Introduction to probability theory and statistical inference by Larson, HaroldNOTE: 6.011 Signals, Systems, and Inferenence found in MIT OCW
18.05MathIntroduction to Probability and StatisticsSyllabusIntroduction to Probability and StatisticsIntroduction to Probability and Statistics
Centers
Select five subjects, including one from each area:
Data-centric
6.372MathIntroduction to Statistical Data AnalysisSyllabusTamhane, Ajit C., and Dorothy D. Dunlop. Statistics and Data Analysis: From Elementary to Intermediate. Prentice Hall, 1999. ISBN: 9780137444267.Statistics and Data Analysis: From Elementary to Intermediate. Prentice Hall, 2000NOTE: 15.075J Statistical Thinking and Data Analysis found in MIT OCW
6.39EECSIntroduction to Machine LearningIntroduction to Machine Learning through MITOpenLearningLibraryNOTE: 6.036 Introduction to Machine Learning found in MIT OCW
Model-centric
6.3EECSSignal ProcessingSyllabusOppenheim, Alan, and Alan Willsky. Signals and Systems. 2nd ed. Prentice Hall, 1996. ISBN: 9780138147570.Introduction to signals and systems by Lindner, Douglas K 1999NOTE: 6.003 Signals and Systems found in MIT OCW
6.411EECSRepresentation, Inference, and Reasoning in AI 2SyllabusWinston, Patrick Henry. Artificial Intelligence. 3rd ed. Addison-Wesley, 1992. ISBN: 9780201533774.Winston, Patrick Henry. Artificial Intelligence. 3rd ed. Addison-Wesley, 1992. ISBN: 9780201533774.NOTE: 6.034 Artificial Intelligence found in MIT OCW
6.44EECSComputer Graphics 3Syllabusnone requiredWatt, Alan. 3D Computer Graphics. Addison-Wesley, 1999. ISBN: 9780201398557.NOTE: 6.837 Computer Graphics found in MIT OCW
Decision-centric
6.31EECSDynamical System Modeling and Control DesignSyllabusDr. Kent Lundberg’s Notes on Feedback Systems.Feedback control systems by Phillips, Charles LNOTE: 6.302 Feedback Systems (graduate version of course) found in MIT OCW
6.411Representation, Inference, and Reasoning in AI 2Syllabus(Coursera) Probabilistic Graphical Models from Stanford UniversityNOTE: 6.438 Algorithms for Inference (introductory graduate course) found in MIT OCW
6.7201EECSOptimization Methods 4SyllabusAMPL Student Version DownloadSee syllabus for links to PDFsNOTE: 6.255J Optimazation Methods (graduate course) found in MIT OCW
Computation-centric
6.1220[J]EECSDesign and Analysis of AlgorithmsSyllabusIntroduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (Third Edition, MIT Press) ISBN: 9780262033848NOTE: 6.046J Design and Analysis of Algorthims found in MIT OCW
6.44EECSComputer Graphics 3Syllabusnone requiredWatt, Alan. 3D Computer Graphics. Addison-Wesley, 1999. ISBN: 9780201398557.NOTE: 6.837 Computer Graphics found in MIT OCW
6.7201EECSOptimization Methods 4SyllabusAMPL Student Version DownloadSee syllabus for links to PDFsNOTE: 6.255J Optimazation Methods (graduate course) found in MIT OCW
Human-centric
6.3260[J]EECSNetworksSyllabusNewman, Mark. Networks: An Introduction. Oxford University Press, 2010. ISBN: 9780199206650.Easley, David and Jon Kleinberg. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, 2010. ISBN: 9780521195331.NOTE: 14.015J Networks found in MIT OCW
6.395AI, Decision Making, and Society
6.4120[J]EECSComputational Cognitive ScienceSyllabusRussell, Stuart J., and Peter Norvig. Artificial Intelligence: A Modern Approach. 2nd ed., 2003.Russell, Stuart J., and Peter Norvig. Artificial Intelligence: A Modern Approach. 3rd ed ,2010NOTE: 9.66(J) Computational Cognitive Science found in MIT OCW
6.4590[J]EECSFoundations of Information PolicySyllabusSee Readings in the SyllabusBrin, David. The Transparent Society: Will Technology Force Us to Choose Between Privacy and Freedom? New York, NY: Perseus Books, 1999. ISBN: 0738201448.NOTE: 6.805 Ethics and the Law on he Electronic Frontier found in MIT OCW
Communication-intensive in the Major
Select one of the following:
6.UATOral Communication (CI-M)SyllabusPerelman, Paradis, and Barrett. The Mayfield Handbook of Technical and Scientific Writing. McGraw-Hill, 1997.Scientific and technical writing today : from problem to proposal by Magrino, William 2nd ed, 2013NOTE: 21W.780 Communication in a Technical Organizations found in MIT OCW
6.UARSeminar in Undergraduate Advanced Research (12 units, CI-M)
Application CI-M
Select two of the following or one of the following plus one AI+D advanced course:
6.4200[J]EECSRobotics: Science and Systems (CI-M)SyllabusAsada, H., and J. J. Slotine. Robot Analysis and Control. New York, NY: Wiley, 1986. ISBN: 9780471830290.NOTE: 2.12 Introduction to Robotics found in MIT OCW
6.421EECSRobotic Manipulation (CI-M)SyllabusReadingsNOTE: 6.834J Cognitive Robots found in MIT OCW
6.8301EECSAdvances in Computer Vision (CI-M)SyllabusNOTE: 8.801 Machine Vision found in MIT OCW
6.8611EECSQuantitative Methods for Natural Language Processing (CI-M)SyllabusJurafsky, David, and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. 2016NOTE: 6.854 Advanced Natural Language Processing found in MIT OCW
Social and Ethical Responsibilities of Computing (SERC)-qualified Subjects (select 1)
6.39Introduction to Machine Learning6.867 SyllabusSee 6.867 Lecture Notes6.036 Introduction to Machine Learning through MITOpenLearningLibraryNOTE: two courses 6.036 and 6.867 Introduction to Machine Language found in MIT OCW
6.395AI, Decision Making, and Society
Foundations of Information PolicySyllabusSee Readings in the SyllabusBrin, David. The Transparent Society: Will Technology Force Us to Choose Between Privacy and Freedom? New York, NY: Perseus Books, 1999. ISBN: 0738201448.NOTE: 6.805 Ethics and the Law on he Electronic Frontier found in MIT OCW
6.8301Advances in Computer Vision (CI-M)SyllabusNOTE: 8.801 Machine Vision found in MIT OCW
6.8611Quantitative Methods for Natural Language Processing (CI-M)SyllabusJurafsky, David, and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. 2016NOTE: 6.854 Advanced Natural Language Processing found in MIT OCW
AI+D Advanced Undergraduate Subjects (select 1 or a second Application CI-M course)
Statistics, Computation and Applications
6.5151Large-scale Symbolic Systems 1
6.5831Database Systems 1SyllabusHellerstein, Joseph, and Michael Stonebraker. Readings in Database Systems (The Red Book) . 5th ed. 2015Ramakrishnan, Raghu, and Johannes Gehrke. Database Management Systems. 3rd ed. McGraw-Hill, 2002.NOTE: 6.830 Databse Systems found in MIT OCW
6.8371Digital and Computational PhotographySyllabusSee ReadingsNOTE: MAS 131 Computational Camera and Photography found in MIT OCW
6.8701Computational Biology: Genomes, Networks, EvolutionSyllabusSee ReadingsNOTE: 6.047 Computational Biology found in MIT OCW
Computational Systems Biology: Deep Learning in the Life Sciences 16.S191 Introduction to Deep LearningNOTE: 6.S191 Introduction to Deep Learning found in MIT OCW
18.404MathTheory of Computation 1SyllabusSipser, Michael. Introduction to the Theory of Computation. 3rd ed. Cengage Learning, 2012. ISBN: 9781133187790.Sipser, Michael. Introduction to the Theory of Computation., 1997
Select one additional from the EECS list or a Math (course 18) requirement
General Institute Requirements (GIRs)
The General Institute Requirements include a Communication Requirement that is integrated into both the HASS Requirement and the requirements of each major; see details below.
Summary of Subject RequirementsSubjects
Science Requirement6
Humanities, Arts, and Social Sciences (HASS) Requirement; at least two of these subjects must be designated as communication-intensive (CI-H) to fulfill the Communication Requirement.8
Restricted Electives in Science and Technology (REST) Requirement [satisfied by 6.1200[J] and 18.C06 in the Departmental Program]2
Laboratory Requirement (12 units) [satisfied by 6.1010 in the Departmental Program]1
Total GIR Subjects Required for SB Degree17
Physical Education Requirement
Swimming requirement, plus four physical education courses for eight points.

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