SYMSYS 161

Applied Symbolic Systems in VC + Entrepreneurship

2 Units, Thursdays 11:30AM - 1:20PM, 320-105

A weekly seminar mapping key trends in computational frameworks towards entrepreneurship and venture capital.

Syllabus

A weekly seminar allowing students the opportunity to discuss and explore applied Symbolic Systems in Technology, Entrepreneurship, and Venture Capital. We will explore popular conventions and trends vis-a-vis the lens of numerous deductive and applied Symbolic Systems. The course explores disparate models to interrogate trends in Artificial Intelligence, technology in Silicon Valley, associated economic cycles, and Venture Capital theses. 

There will be weekly readings and an end of quarter project. Preference will given to upperclassmen and graduate students with a demonstrated interest in technology, entrepreneurship, and finance.

Time: Spring Quarter, Thursdays: 11:30-1:20PM

Location: BLDG 320, ROOM 105 

Office Hours: Held through Twitter, Fridays 4-6PM. Tweet @nanli and @zavaindar with '#symsys161'

Prerequisites: Upperclassman and graduate students

Units: 2

Dates:
4/29 11:59PM PST - Deadline: Final Project Proposal
5/27 11:59PM PST - Deadline: Final Project

 

Section 1: Platonic Realism to Data Nihilism: Overview of shift from a deductive, parameterized approach to Bayesian, statistical view.

  • Brief anthropological look on early Artificial Intelligence, computational AI, AI Winter, and Bayesian Revolution

  • What is big data, how do we characterize it?

  • How do recent inflections from AI map to Silicon Valley trends?

  • How do recent data trends enable further traction in applied AI and Symbolic Systems?

Section 2: Modeling Silicon Valley: The last 10 years have come full cycle in Silicon Valley. From the depths of the recession to the unprecedented rise of "Unicorns", there is much to be reviewed and discussed.

  • Technology drivers and key trends

  • Economic forces and VC heuristics

  • An emerging down cycle

Section 3: Model Building in Venture Capital: This dual approach to problem solving can be applied to the investment process itself. Introduce and establish select array of investment frameworks

  • Frameworks

  • Grading Venture Firms

  • How are these investment thesis driving technology progress forward, and in what sense do the models themselves have an effect on future data and outcomes?

Section 4: Case Studies: Evaluate select tech startups through the presented frameworks

Section 5: Big Data and Investment Nihilism: Can we model shifts in venture itself?

  • Examine recent funding trends, Angellist, Bitcoin, and Macro economic trends to characterize shifting pieces undermining parametric venture model