Condensed AI Challenge: 2 week progress

The best laid schemes o' Mice an' Men 

Gang aft agley

Robert Burns To a Mouse

Made both more progress and less progress than planned.  I bounced around my plan: Artificial Intelligence (AI) article here, Machine Learning (ML) video there; Linear Algebra course here, Python book there. Also, I used more than my scheduled 10 hours a week (more like 30). The long and short of it is that 2 weeks of study gave me an overview glimpse of Artificial Intelligence (the 'more' progress) and confirmed MIT curriculum: I need to learn Linear Algebra and Python first (the less progress).

What is a determinant?

While slugging away at Linear Algebra I hit a roadblock in Imperial College London's Mathematics for Machine learning (coursera). So I backed up by taking a simpler course: Georgia Tech's Linear Algebra II (Edx) and reading a linear algebra chapter in a college math book. I continue to hit roadblocks in Mathematics for Machine Learning; the current one requires side reading on matrix transforms. 

For Python, the Python in 30 minutes course (it has 2 hours of lectures, go figure), and Python for Beginners book gave me enough to start understanding the code. The courses that I have listed include exercises and projects using Python which will deepen my learning.

Here is my updated plan (Yellow items are Phase I tasks; Blue items have been completed). The Orange items are documentaries that I started watching but don't provide the practical content I'm looking for. The Green item I've read but needs a revisit after I learn Machine Learning concepts better.

Resources for Learning AI, ML, DL
SUBJECT	Internet	YouTube	Book	Online Course
Python	Python for Beginners	Learn Python - Full Course for Beginners [Tutorial]	Python crash course: a hands-on, project-based introduction to programming	Development Programming Languages Python Python from Beginner to Intermediate in 30 min.
	Best Way to Learn Python in 2022 (Free and Paid Python Tutorials)	Python Tutorial - Python Full Course for Beginners	Automate the boring stuff with Python: practical programming for total beginners	Coursera: Python for Everybody Specialization
			Python for Beginners: A Smarter Way to Learn Python in 5 Days
Linear Algebra	What is linear algebra, Chapter 1(LibreTexts:Mathematics Mar 2021)	Essence of Linear Algebra	Linear Algebra and Its Applications by David Lay 5th Edition	Coursera: Mathematics for Machine Learning: Linear Algebra
	A Gentle Introduction to Linear Algebra (MachineLearningMastery Aug 2019)	Essence of Linear Algebra		Edx Linear Algebra II: Matrix Algebra
Artificial Intelligence	Oracle: What is AI?	Artificial Intelligence in 5 minutes	AI for Dummies by John Paul Mueller, Luca Massaron (2021)	Coursera: AI for Everyone (Beginner 12 hrs)
	IBM Cloud Education (June 2020)	Artificial intelligence and algorithms: pros and cons	Life 3.0: Being Human in the Age of Artifiucial Intelligence by Max Tegmarck (2017)	IBM: Introduction to AI (Beginner 11 hrs)
	Stanford: Artificial Intelligence		Superintelligence: Paths, Dangers, Strategies by Nick Bolstrom 2016	edX: AI for Everyone: Master the Basics (Beginner 8 hrs)
Machine Learning	Machine Learning Explained in 3 minutes (2017)	Computer Scientist Explains Machine Learning in 5 Levels of Difficulty	Machine Learning for Dummies by John Paul Mueller, Luca Massaron (2016)	Coursera: Machine Learning for All (Beginner 22hrs)
	MIT: Machine learning, explained by Sara Brown (Apr 2021)	Machine Learning: Living in the Age of AI | A WIRED Film	Machine Learning by Tom M. Mitchell (1997 Internet Archives)	IBM: Machine Learning with Python: A Practical Introduction (30 hrs)
	MathWorks: What Is Machine Learning? How it works, why it matters, and getting started	Machine Learning & Artificial Intelligence: Crash Course Computer Science #34	Machine Learning using Python by U Dinesh Kumar	Stanford: Machine Learning Specialization (3 courses, 3 mns@9 hrs/wk)
Deep Learning	Deep Learning by: IBM Cloud Education (May 2020)	Deep Learning In 5 Minutes	Deep Learning A Visual Approach by Phenix40 (2021 Internet Archives)	Coursera: Introduction to Deep Learning (Intermediate 60Hrs)
	MathWorks: What Is Deep Learning? 3 things you need to know		Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python	edX/IBM: Deep Learning Fundamentals with Keras (20hrs)
	What is Deep Learning? by Jason Brownlee on Aug 2020	Neural Networks (the first 2 videos address deep learning)	Neural Networks and Deep Learning by Michael Nielsen (Dec 2019)	Deep Learning Specialization (5 courses, 5 mns@9 hrs/wk)

Artificial Intelligence Challenge - condensed version

After compiling the Artificial Intelligence Challenge I realized that I'm not interested in completing the whole program. I also want to get a deeper understanding of AI and ML than what reading a couple of 'for Dummies' books will give me. To do this I will have to learn some foundational material: Python and Linear Algebra.

My plan is to do the following Artificial Intelligence Challenge - condensed version::

PHASE 1 (5 weeks @8-10hrs/wk)

1. Python: view the YouTube videos (11hrs)
2. Linear Algebra: read the 2 internet pages and complete the first 8 lessons of Essence of Linear Algebra (4hrs)
3. Artificial Intelligence: read the 3 internet pages (3hrs), watch YouTube videos (1hr), and read AI for dummies (5hrs) - TOTAL 9hrs
4. Machine Language: Read the 3 internet pages (2hrs), watch YouTube videos (2hrs), and read ML for dummies (7hrs) - TOTAL 11hrs
5. Deep Learning: Read the 3 internet pages (1hr), watch YouTube videos (2hrs), and read Deep Learning: A Visual Approach (7hrs) - TOTAL 10hrs

PHASE 2 (7 weeks @8-10hrs/week)

1. Python: read Python Crash Course (10hrs) and complete Udemy: Python from Beginner to Intermediate in (1hr) - TOTAL 11hrs
2. Linear Algebra: complete Coursera: Mathematics for Machine Learning: Linear Algebra (19hrs)
3. Artificial Intelligence: Coursera: AI for Everyone (12hrs)
4. Machine Language: Coursera: Machine Learning for All (22hrs)
5. Deep Learning: edX/IBM: Deep Learning Fundamentals with Keras (20hrs)

Based on this my target completion is the end on 2022. At that time I'll determine if I've achieved the level of understanding I want.

Resources for Learning AI, ML, DL
SUBJECT	Internet	YouTube	Book	Online Course
Python	Python for Beginners	Learn Python - Full Course for Beginners [Tutorial]	Python crash course: a hands-on, project-based introduction to programming	Udemy: Python from Beginner to Intermediate in 30 min.
	Best Way to Learn Python in 2022 (Free and Paid Python Tutorials)	Python Tutorial - Python Full Course for Beginners	Automate the boring stuff with Python: practical programming for total beginners	Coursera: Python for Everybody Specialization
Linear Algebra	What is linear algebra (LibreTexts:Mathematics Mar 2021)	Essence of Linear Algebra	Linear Algebra and Its Applications by David Lay 5th Edition	Mathematics for Machine Learning: Linear Algebra
	A Gentle Introduction to Linear Algebra (MachineLearningMastery Aug 2019)
Artificial Intelligence	Oracle: What is AI?	Artificial Intelligence in 5 minutes	AI for Dummies by John Paul Mueller, Luca Massaron (2021)	Coursera: AI for Everyone (Beginner 12 hrs)
	IBM Cloud Education (June 2020)	Artificial intelligence and algorithms: pros and cons	Life 3.0: Being Human in the Age of Artifiucial Intelligence by Max Tegmarck (2017)	IBM: Introduction to AI (Beginner 11 hrs)
	Stanford: Artificial Intelligence		Superintelligence: Paths, Dangers, Strategies by Nick Bolstrom 2016	edX: AI for Everyone: Master the Basics (Beginner 8 hrs)
Machine Learning	Machine Learning Explained in 3 minutes (2017)	Computer Scientist Explains Machine Learning in 5 Levels of Difficulty	Machine Learning for Dummies by John Paul Mueller, Luca Massaron (2016)	Coursera: Machine Learning for All (Beginner 22hrs)
	MIT: Machine learning, explained by Sara Brown (Apr 2021)	Machine Learning: Living in the Age of AI | A WIRED Film	Machine Learning by Tom M. Mitchell (1997 Internet Archives)	IBM: Machine Learning with Python: A Practical Introduction (30 hrs)
	MathWorks: What Is Machine Learning? How it works, why it matters, and getting started	Machine Learning & Artificial Intelligence: Crash Course Computer Science #34	Machine Learning using Python by U Dinesh Kumar	Stanford: Machine Learning Specialization (3 courses, 3 mns@9 hrs/wk)
Deep Learning	Deep Learning by: IBM Cloud Education (May 2020)	Deep Learning In 5 Minutes	Deep Learning A Visual Approach by Phenix40 (2021 Internet Archives)	Coursera: Introduction to Deep Learning (Intermediate 60Hrs)
	MathWorks: What Is Deep Learning? 3 things you need to know		Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python	edX/IBM: Deep Learning Fundamentals with Keras (20hrs)
	What is Deep Learning? by Jason Brownlee on Aug 2020	Neural Networks (the first 2 videos address deep learning)	Neural Networks and Deep Learning by Michael Nielsen (Dec 2019)	Deep Learning Specialization (5 courses, 5 mns@9 hrs/wk)

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
NUMBER	CATEGORY	NAME	RESOURCES	TEXTBOOK	ONLINE	NOTES
Departmental Program
Choose at least two subjects in the major that are designated as communication-intensive (CI-M) to fulfill the Communication Requirement.
6.100A	Progamming Skills	Introduction to Computer Science Programming in Python	Syllabus	Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data Second Edition. MIT Press, 2016. ISBN: 9780262529624	An Introduction to Programming Using Python by David Schneidern in Internet Archives
6.121	Foundation	Introduction to Algorithms	Syllabus	Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (Third Edition, MIT Press) ISBN: 9780262033848	Data Science Programming in Python by Anita Raichand	NOTE: 6.006 found in MIT OCW
6.101	Foundation	Fundamentals of Programming	Syllabus	Guttag, 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 Archives	NOTE: 6.00SC found in MIT OCW
6.1200[J]	Math	Mathematics for Computer Science	Syllabus	Mathematics for Computer Science	MIT Online Publication
18.C06	Math	Linear Algebra and Optimization 1	Syllabus	Strang, Gilbert. Introduction to Linear Algebra. 5th ed. Wellesley, MA: Wellesley-Cambridge Press , February 2016. ISBN: 9780980232776	Strang, Gilbert. Introduction to Linear Algebra. 4th ed.	NOTE: 18.06SC found in MIT OCW
Select one of the following:
6.37	Math	Introduction to Probability	Introduction to Probability	Introduction to Probability	Introduction to Probability	NOTE: RES.6-012 found in MIT OCW
6.38	Math	Introduction to Inference	Syllabus	Oppenheim, Alan, and George Verghese. Signals, Systems and Inference. Pearson, 2017. ISBN: 9781292156200	Introduction to probability theory and statistical inference by Larson, Harold	NOTE: 6.011 Signals, Systems, and Inferenence found in MIT OCW
18.05	Math	Introduction to Probability and Statistics	Syllabus	Introduction to Probability and Statistics	Introduction to Probability and Statistics
Centers
Select five subjects, including one from each area:
Data-centric
6.372	Math	Introduction to Statistical Data Analysis	Syllabus	Tamhane, 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, 2000	NOTE: 15.075J Statistical Thinking and Data Analysis found in MIT OCW
6.39	EECS	Introduction to Machine Learning			Introduction to Machine Learning through MITOpenLearningLibrary	NOTE: 6.036 Introduction to Machine Learning found in MIT OCW
Model-centric
6.3	EECS	Signal Processing	Syllabus	Oppenheim, Alan, and Alan Willsky. Signals and Systems. 2nd ed. Prentice Hall, 1996. ISBN: 9780138147570.	Introduction to signals and systems by Lindner, Douglas K 1999	NOTE: 6.003 Signals and Systems found in MIT OCW
6.411	EECS	Representation, Inference, and Reasoning in AI 2	Syllabus	Winston, 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
6.44	EECS	Computer Graphics 3	Syllabus	none required	Watt, Alan. 3D Computer Graphics. Addison-Wesley, 1999. ISBN: 9780201398557.	NOTE: 6.837 Computer Graphics found in MIT OCW
Decision-centric
6.31	EECS	Dynamical System Modeling and Control Design	Syllabus	Dr. Kent Lundberg’s Notes on Feedback Systems.	Feedback control systems by Phillips, Charles L	NOTE: 6.302 Feedback Systems (graduate version of course) found in MIT OCW
6.411		Representation, Inference, and Reasoning in AI 2	Syllabus		(Coursera) Probabilistic Graphical Models from Stanford University	NOTE: 6.438 Algorithms for Inference (introductory graduate course) found in MIT OCW
6.7201	EECS	Optimization Methods 4	Syllabus	AMPL Student Version Download	See syllabus for links to PDFs	NOTE: 6.255J Optimazation Methods (graduate course) found in MIT OCW
Computation-centric
6.1220[J]	EECS	Design and Analysis of Algorithms	Syllabus	Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (Third Edition, MIT Press) ISBN: 9780262033848		NOTE: 6.046J Design and Analysis of Algorthims found in MIT OCW
6.44	EECS	Computer Graphics 3	Syllabus	none required	Watt, Alan. 3D Computer Graphics. Addison-Wesley, 1999. ISBN: 9780201398557.	NOTE: 6.837 Computer Graphics found in MIT OCW
6.7201	EECS	Optimization Methods 4	Syllabus	AMPL Student Version Download	See syllabus for links to PDFs	NOTE: 6.255J Optimazation Methods (graduate course) found in MIT OCW
Human-centric
6.3260[J]	EECS	Networks	Syllabus	Newman, 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
6.395		AI, Decision Making, and Society
6.4120[J]	EECS	Computational Cognitive Science	Syllabus	Russell, 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 ,2010	NOTE: 9.66(J) Computational Cognitive Science found in MIT OCW
6.4590[J]	EECS	Foundations of Information Policy	Syllabus	See Readings in the Syllabus	Brin, 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.UAT		Oral Communication (CI-M)	Syllabus	Perelman, 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, 2013	NOTE: 21W.780 Communication in a Technical Organizations found in MIT OCW
6.UAR		Seminar 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]	EECS	Robotics: Science and Systems (CI-M)	Syllabus	Asada, 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
6.421	EECS	Robotic Manipulation (CI-M)	Syllabus		Readings	NOTE: 6.834J Cognitive Robots found in MIT OCW
6.8301	EECS	Advances in Computer Vision (CI-M)	Syllabus			NOTE: 8.801 Machine Vision found in MIT OCW
6.8611	EECS	Quantitative Methods for Natural Language Processing (CI-M)	Syllabus	Jurafsky, David, and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. 2016		NOTE: 6.854 Advanced Natural Language Processing found in MIT OCW
Social and Ethical Responsibilities of Computing (SERC)-qualified Subjects (select 1)
6.39		Introduction to Machine Learning	6.867 Syllabus	See 6.867 Lecture Notes	6.036 Introduction to Machine Learning through MITOpenLearningLibrary	NOTE: two courses 6.036 and 6.867 Introduction to Machine Language found in MIT OCW
6.395		AI, Decision Making, and Society
6.4590[J]		Foundations of Information Policy	Syllabus	See Readings in the Syllabus	Brin, 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
6.8301		Advances in Computer Vision (CI-M)	Syllabus			NOTE: 8.801 Machine Vision found in MIT OCW
6.8611		Quantitative Methods for Natural Language Processing (CI-M)	Syllabus	Jurafsky, David, and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. 2016		NOTE: 6.854 Advanced Natural Language Processing found in MIT OCW
AI+D Advanced Undergraduate Subjects (select 1 or a second Application CI-M course)
6.3730[J]		Statistics, Computation and Applications
6.5151		Large-scale Symbolic Systems 1
6.5831		Database Systems 1	Syllabus	Hellerstein, Joseph, and Michael Stonebraker. Readings in Database Systems (The Red Book) . 5th ed. 2015	Ramakrishnan, Raghu, and Johannes Gehrke. Database Management Systems. 3rd ed. McGraw-Hill, 2002.	NOTE: 6.830 Databse Systems found in MIT OCW
6.8371		Digital and Computational Photography	Syllabus	See Readings		NOTE: MAS 131 Computational Camera and Photography found in MIT OCW
6.8701		Computational Biology: Genomes, Networks, Evolution	Syllabus	See Readings		NOTE: 6.047 Computational Biology found in MIT OCW
6.8711[J]		Computational Systems Biology: Deep Learning in the Life Sciences 1			6.S191 Introduction to Deep Learning	NOTE: 6.S191 Introduction to Deep Learning found in MIT OCW
18.404	Math	Theory of Computation 1	Syllabus	Sipser, 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 Requirements				Subjects
Science Requirement				6
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 Degree				17
Physical Education Requirement
Swimming requirement, plus four physical education courses for eight points.