1. Machine Learning: Machine Learning, or ML for short
Note: The first of the 12 IT skills that need to be mastered, refer to the link https://blog.csdn.net/a2806005024/article/details/41285975
2. Learning objectives: understand ML; apply ML algorithms to practical problems; continue to do ML research
Note: It can be seen that this is just an introductory tutorial for ML
3. Requirements: basic computer knowledge, skills and principles; data structures (linked lists, teams, stacks, binary trees); basic coding skills; knowledge of probability and statistics (random variables, expectations of random variables, variance, etc.); knowledge of linear algebra (matrix, vector, eigenvector, vector machine, etc.)
4. Assignment: The group completes a graduation project
2. Definition of Machine Learning
1. ML的定义：A computer program is said to learn from experience E with respect to some task T and some performance measure P. If its performance on T ,as measured by P, improves with experience E.
Chinese understanding: For a computer program, given a task T and a performance measurement value P, if, under the influence of experience E, after P evaluation, the performance of the program in processing T has improved, then the program is said to be from learning in E.
3. Several parts of machine learning
1. Supervised learning (supervised learning) such as: regression problems; classification problems
2. Unsupervised learning e.g. clustering algorithms
3. Reinforcement learning e.g. reward function