Lecture 1 Computers and programming languages introduction

Lab 1 Basics of python

Lecture 2 Program flow control

Lecture 3 Functions and scripts

Lab 2 Debugging and troubleshooting

Labor day

Lab 3 Using Visual Studio Code IDE

Lab 4 Using Visual Studio Code IDE

Lecture 4 Data reduction and fitting

Lecture 5 Data reduction and fitting practice (AI driven)

Lab 5 Working on homework

Lecture 6 Root finding

Lecture 7 Integration

Lab 6 the Good, the Bad report, and the Ugly AI generated reports

Lab 7 Integrals and root finding gotchas

Lecture 8 Monte Carlo integration

Lab 8 Midterm mock exam

Midterm

Lecture 9 Random numbers

Fall break

Fall break

Fall break

Fall break

Lecture 10 AI driven simulation of a colony part 1

Lecture 11 AI driven simulation of a colony part 2

Lab 9 AI driven simulation of a colony part 3

Lecture 12 The method which rule them all - Optimization, part 1 combinatorics

Lecture 13 Optimization part 2, smooth functions

Lecture 14 Optimization part 3, noisy problems

Last day to withdraw

Lecture 15 Optimization part 4, demystifying neural networks and AI

Lab 10 Optimization practice

Lecture 16 Solving system of linear equations

Lecture 17 Speeding up calculations

Election day

Midterm

Lecture 18 Interpolation (sometimes we do not need AI)

Lecture 19 Ordinary differential equations part 1

Lab 11 practice

Lecture 20 Ordinary differential equations part 2

Lecture 21 Ordinary differential equations part 3

Lab 12 practice

Lecture 22 Digital Fourier Transform (DFT)

Remote day

Lecture 23 DFT filters

Thanksgiving break

Thanksgiving break

Thanksgiving break

Thanksgiving break

Thanksgiving break

Lab 13 practice

Lecture 24 DFT and convolution

Lecture 25 Quantum Computing

2pm Final for 1pm section

9am Final for 11am section