Hand-on, Minds On with Dr. Christopher Terman (S1:E6)
Skills:
Staying Current in AI60%
MIT Chalk Radio, Season 1
Instructor: Christopher Terman, Sarah Hansen
Subscribe here → https://chalk-radio.simplecast.com/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63YwKIMA9K08FFvdeBEl6Lo
You might imagine that fluency is an inherently good thing in teaching. But Dr. Christopher Terman, Senior Lecturer Emeritus at MIT’s Computer Science & Artificial Intelligence Lab, explains that breaks in the flow of the classroom can actually make the learning experience more memorable. This is just one of the insights Dr. Terman has gained in twenty years of teaching the course 6.004 Computation Structures. “If you’re going to spend 40% of your time in the classroom,” he says, “you might as well teach well.” He and the rest of the teaching team for 6.004 are always seeking to optimize their students’ learning experience, adapting the course through repeated iterations to include as much as possible of what they’ve found works best. Among the details Dr. Terman shares in this episode are how the course engages students from different backgrounds by offering a “buffet” of learning materials through the use of the MITx learning platform, how creating hands-on browser-based digital design lab experiences help students internalize the material, and how online forums reduce student frustration by offering quicker answers to questions that arise outside of class.
Relevant Resources
MIT OpenCourseWare
https://ocw.mit.edu/index.htm?utm_source=simplecast&utm_medium=shownotes&utm_campaign=chalkradio&utm_term=s1e6
The OCW Educator Portal
https://ocw.mit.edu/educator/?utm_source=simplecast&utm_medium=shownotes&utm_campaign=chalkradio&utm_term=s1e6
6.004 on OCW
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-004-computation-structures-spring-2017/?utm_source=simplecast&utm_medium=shownotes&utm_campaign=chalkradio&utm_term=s1e6
Dr. Terman’s Faculty Page
http://people.csail.mit.edu/cjt/
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21. Post Trade Clearing, Settlement & Processing
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10. Financial System Challenges & Opportunities
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7. Technical Challenges
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3. Blockchain Basics & Cryptography
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19. Primary Markets, ICOs & Venture Capital, Part 1
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1. Introduction for 15.S12 Blockchain and Money, Fall 2018
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Chalk Radio, A Podcast about Inspired Teaching at MIT (Teaser)
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Nuclear Gets Personal with Prof. Michael Short (S1:E1)
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How Africa Has Been Made to Mean with Prof. Amah Edoh (S1:E2)
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Making Deep Learning Human with Prof. Gilbert Strang (S1:E3)
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Social Impact at Scale, One Project at a Time with Dr. Anjali Sastry (S1:E4)
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Film is for Everyone with Prof. David Thorburn (S1:E5)
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Lecture 3: Learning to Fly
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Lecture 13: Interpreting Weather Data
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Lecture 21: Weather Minimums and Final Tips
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Hand-on, Minds On with Dr. Christopher Terman (S1:E6)
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Part 4: Eigenvalues and Eigenvectors
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Part 5: Singular Values and Singular Vectors
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Part 3: Orthogonal Vectors
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Part 2: The Big Picture of Linear Algebra
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Part 1: The Column Space of a Matrix
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Intro: A New Way to Start Linear Algebra
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9. Chromatin Remodeling and Splicing
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28. Visualizing Life - Fluorescent Proteins
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20. Roth's theorem III: polynomial method and arithmetic regularity
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8. Szemerédi's graph regularity lemma III: further applications
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19. Roth's theorem II: Fourier analytic proof in the integers
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12. Pseudorandom graphs II: second eigenvalue
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1. A bridge between graph theory and additive combinatorics
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Special Episode: Teaching Remotely During Covid-19 with Prof. Justin Reich
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Spring 2020 Update from Dean Rajagopal
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S1E7: Unpacking Misconceptions about Language & Identities with Prof. Michel DeGraff
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Climate 101 Live
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Welcome for Volunteers (for EarthDNA's Climate 101)
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Learning to Fly with Drs. Philip Greenspun & Tina Srivastava (S1:E8)
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Thinking Like an Economist with Prof. Jonathan Gruber (S1:E9)
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2. Cyber Network Data Processing; AI Data Architecture
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1. Artificial Intelligence and Machine Learning
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2: Resistor Capacitor Circuit and Nernst Potential - Intro to Neural Computation
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14: Rate Models and Perceptrons - Intro to Neural Computation
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4: Hodgkin-Huxley Model Part 1 - Intro to Neural Computation
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18: Recurrent Networks - Intro to Neural Computation
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3: Resistor Capacitor Neuron Model - Intro to Neural Computation
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15: Matrix Operations - Intro to Neural Computation
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13: Spectral Analysis Part 3 - Intro to Neural Computation
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16: Basis Sets - Intro to Neural Computation
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20: Hopfield Networks - Intro to Neural Computation
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8: Spike Trains - Intro to Neural Computation
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7: Synapses - Intro to Neural Computation
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19: Neural Integrators - Intro to Neural Computation
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5: Hodgkin-Huxley Model Part 2 - Intro to Neural Computation
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6: Dendrites - Intro to Neural Computation
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17: Principal Components Analysis_ - Intro to Neural Computation
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12: Spectral Analysis Part 2 - Intro to Neural Computation
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11: Spectral Analysis Part 1 - Intro to Neural Computation
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9: Receptive Fields - Intro to Neural Computation
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10: Time Series - Intro to Neural Computation
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1: Course Overview and Ionic Currents - Intro to Neural Computation
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The Power of OER with Profs. Mary Rowe and Elizabeth Siler (S1:E10)
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