47-779 / 47-785, 18-819F course syllabus - Fall 2022

Lectures: Slides

• Lecture 0: Course Overview.
  Slides

• Lecture 1: Review of Linear Algebra I.
  Slides

• Lecture 2: Review of Linear Algebra II.
  Slides

• Lecture 3: Introduction to Machine Learning.
  Slides

• Lecture 4: Deep Learning.
  Slides

• Lecture 5: Convolutional Neural Networks.
  Slides

• Lecture 5.1: Convolutional Neural Networks Tutorial.
  Slides

• Lecture 6: Mathematical Programming.
  Slides

• Lecture 7: Graver Augmented Multiseed Algorithm (GAMA).
  Slides

• Lecture 8: Ising and QUBO.
  Slides

• Lecture 9: Quantum Annealing, Quantum-Inspired Heuristics, Benchmarking, and Parameter settings.
  Slides

• Lecture 10: Axioms of Quantum Mechanics.
  Slides

• Lecture X: Subscribing to AWS Braket, DWave, IBM Qiskit and USRA RIACS.
  Slides

• Lecture 11: Introduction to Quantum Gates and Circuits.
  Slides

• Lecture 12: NISQ Optimization.
  Slides

• Lecture 13: First Look at Quantum Algorithms Deutsch’s Problem.
  Slides

• Lecture 14: Quantum Fourier Transform.pdf.
  Slides

• Lecture 15: Quantum Phase Estimation and Linear Algebraic Systems.pdf.
  Slides