Concept:

Machine Learning (ML) is a multidisciplinary discipline, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in the study of how computers simulate or realize human learning behavior to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve its own performance.

It is the core of artificial intelligence, is the fundamental way to make the computer intelligent, its application throughout the various fields of artificial intelligence, it mainly uses induction, synthesis rather than deduction.

Teaching course: Introduction to machine learning concepts

(The course focuses on the concepts, principles and application scenarios of machine learning, as well as common algorithms of machine learning, such as supervised learning, unsupervised learning, linear regression, etc.)

Machine learning is a relatively young branch of artificial intelligence research, and its development process can be roughly divided into four periods.

The first stage is in the middle of the 1950s to the middle of the 1960s, belongs to the warm period.

The second phase, from the mid-1960s to the mid-1970s, was known as the cooling-off period for machine learning.

The third stage is from the mid-1970s to the mid-1980s, known as the Renaissance period.

The latest phase of machine learning began in 1986.

Machine learning enters the new stage in the following aspects:

(1) Machine learning has become a new frontier subject and formed a course in universities. It combines psychology, biology and neurophysiology with mathematics, automation and computer science to form the theoretical basis for machine learning. (2) The study of integrated learning system with various learning methods is on the rise. Especially, the coupling of symbol learning can better solve the acquisition and refinement of knowledge and skills in continuous signal processing. (3) A unified view of the fundamental problems of machine learning and artificial intelligence is emerging. For example, the combination of learning and problem solving and the idea that knowledge expression is easy to learn resulted in the block learning of universal intelligent system SOAR. The case-based method combining analogy learning and problem solving has become an important direction of experiential learning. (4) The application of various learning methods is expanding, and some of them have become commodities. The knowledge acquisition tool of inductive learning has been widely used in diagnosis classification expert system. Linkage learning is dominant in phonograph recognition. Analytical learning has been used to design integrated expert systems. Genetic algorithm and reinforcement learning have a good application prospect in engineering control. Neural network connection learning coupled with symbolic system will play an important role in intelligent management and intelligent robot motion planning of enterprises.

(5) The academic activities related to machine learning are unprecedentedly active. In addition to the annual symposium on machine learning, there are also conferences on computer learning theory and genetic algorithms.

The syllabus

Course hours:

Chapter 1: Machine learning concepts, principles and application scenarios

Lesson 1: Basic Concepts of machine learning 16:06

Lesson 2: The Field of Machine Learning 11:50

课时3: why can machines learn

Chapter 2: Common algorithms for machine learning

Period 4: Supervised Learning – Linear regression 14:22

Class 5: Nonlinear regression, overfitting, Model selection

课时6: supervised learning classification 05:26

Period 7: Unsupervised study 12:06

Chapter 3: Summaries and exercises

课时8: summary and practice 03:16

Introduction to the lecturer:

Xi Ting, Senior algorithm expert of Large-scale machine learning at Ant Financial

Course Objectives:

  • Master the concepts, principles and algorithms of machine learning


Suitable for people:

  • Big Data developers
  • Machine learning Developers

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