Completed
Lecture 4.2 - Advanced Classical Machine Learning (ML)
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Quantum Machine Learning - 2021 Qiskit Global Summer School
Automatically move to the next video in the Classroom when playback concludes
- 1 Lecture 1.1 - Vector Spaces, Tensor Products, and Qubits
- 2 Lecture 1.2 - Introduction to Quantum Circuits
- 3 Lecture 2.1 - Simple Quantum Algorithms I
- 4 Lecture 2.2 - Simple Quantum Algorithms II
- 5 Lecture 3.1 - Noise in Quantum Computers - part 1
- 6 Lecture 3.2 - Noise in Quantum Computers - part 2
- 7 Lab 1 - Introduction to Quantum Computing Algorithms and Operations
- 8 Lecture 4.1 - Introduction to Classical Machine Learning (ML)
- 9 Lecture 4.2 - Advanced Classical Machine Learning (ML)
- 10 Lecture 5.1 - Building a Quantum Classifier
- 11 Lecture 5.2 - Introduction to the Quantum Approximate Optimization Algorithm and Applications
- 12 Lab 2 - Introduction to Variational Algorithms
- 13 Lecture 6.1 - From Variational Classifiers to Linear Classifiers
- 14 Lecture 6.2 - Quantum Feature Spaces and Kernels
- 15 Lecture 7.1 - Quantum Kernels in Practice
- 16 Lab 3 - Introduction to Quantum Kernels and Support Vector Machines
- 17 Lecture 8.1 - Introduction and Applications of Quantum Models
- 18 Lecture 8.2 - Barren Plateaus, Trainability Issues, and How to Avoid Them
- 19 Lab 4 - Introduction to Training Quantum Circuits
- 20 Lecture 9.1 - Introduction to Quantum Hardware
- 21 Lecture 9.2 - Hardware Efficient Ansatze for Quantum Machine Learning
- 22 Lab 5 - Introduction to Hardware Efficient Ansatze for Quantum Machine Learning
- 23 Lecture 10.1 - Advanced QML Algorithms
- 24 Lecture 10.2 - The Capacity and Power of Quantum Machine Learning Models
- 25 The Future of Quantum Machine Learning
