Machine learning is a term that is sweeping the world. It captures the popular imagination, conjuring visions of future self-taught artificial intelligence and robotics.
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Machine learning is an umbrella term for a set of techniques and tools that help computers learn and adapt on their own. Machine learning algorithms help AI learn without explicit programming to perform desired actions. Learning models from input samples, machine learning algorithms only predict and perform tasks based on the learned models, rather than predicting and performing tasks through predefined program instructions. Machine learning is a lifesaver in situations where rigorous algorithms cannot be applied. It learns new processes and executes knowledge from previous patterns.
machine learning
The application of machine learning to the field of publishing has also grown rapidly in recent years:
There are many videos and courses of machine learning on the market. This machine learning is not machine learning.
The 2020-2022 academic application course of machine learning has been affirmed by college teachers and classmates. In the summer of 2022, the advanced machine learning course will be introduced to help college students further improve the application skills of more in-depth and cutting-edge machine learning in academia:
· Integrated learning - perfect for structured data
Can be widely used in: time series, medical health, intrusion systems and other related research fields
· Feature engineering - the key to machine learning success
· Deep learning - to solve the problem of manual feature extraction and selection, which is time-consuming and labor-intensive, and depends on experience and luck.
More and more applications in the financial field: stock market forecasting, algorithmic trading, credit risk assessment, portfolio allocation, asset pricing and derivatives markets
Python Summer Teacher Training
Machine Learning Series
Course time and training method
Machine learning online work: 30 hours @ follow the newspaper and learn
Advanced Machine Learning Class: August 12-15 (four days)@remote+recording
→ Sign up for "Machine Learning Online Work" to give Python programming + data cleaning courses
→ Teaching and answering are all by the lecturer himself
→ Different from other Python courses, a Python machine learning academic application course created solely for academic research
Introduction of lecturers
Mr. Chen Yuanxiang , associate professor of Beijing University of Posts and Telecommunications. PhD and postdoctoral fellow at Peking University.
He has published more than 80 SCI/EI academic papers, including more than 40 papers by the first or corresponding author, and applied for 4 invention patents. He presided over the general projects of the National Natural Science Foundation of China, the sub-projects of the National Key R&D Program, the Youth Project of the National Natural Science Foundation of China and the Postdoctoral Fund and other national, provincial and ministerial projects.
IEEE, OSA member, Optics Express, IEEE Photonics Technology Letters, Photonics Journal, Applied Optics and other SCI journal reviewers.
Course content:
Machine Learning Online Course (30 hours):
Part 1: Introduction to Machine Learning Academic Applications:
The basic idea of machine learning
Common machine learning algorithm models
Introduction to Machine Learning Algorithm Libraries
Application scenarios of machine learning in the academic field
Part II: Algorithm Principles and Practice
1. KNN algorithm:
The basic principle of KNN algorithm
Commonly used similarity measurement methods, KNN for classification and regression
KNN model parameter optimization
Python case: KNN for iris dataset classification
2. Decision tree:
Decision Tree Fundamentals
decision tree classification
Decision tree for classification and regression implementation
Decision tree parameter optimization
Python Case: Decision Tree Realizes Boston Housing Price Prediction
3. Linear regression:
Solving Linear Regression, Ridge Regression, LASSO and Elastic Net
Python case: Linear regression realizes abalone age prediction
4. Logistic regression:
Logistic Regression Fundamentals
From Linear Regression to Logistic Regression
Logistic regression implementation and parameter optimization
Python case: logistic regression realizes mortality prediction of sick horses
5. Neural network:
Neural Network Basics
Activation Functions in Neural Networks
Neural network Python implementation and parameter tuning
Python Case: Handwritten Digit Recognition
6. Bayesian network:
Bayesian Classification Principle
Naive Bayes
Bayesian Model Classification
Python Case: Spam Filtering
7. Support vector machine:
Support vector machine classification principle
Linear SVM and Nonlinear SVM
Python case: face recognition
8. Random Forest:
Decision Trees and Random Forests
Random Forest Principle
Random Forest Python Implementation and Parameter Tuning
Python Case: Random Forest for Titanic Wreck Prediction
9. Clustering:
Clustering principle
Clustering and Classification
k-means clustering principle
k-means python implementation
Python case: clustering for customer value identification
Part 3: Instructions for Academic Application of Python Machine Learning
Data discovery and variable creation, prediction, causal inference;
Text big data applications;
Machine learning-based academic paper writing guidance
audition
Advanced Machine Learning Course
(4 days remote + recording and playback):
1. Introduction and application of integrated learning (8 hours)
1. The advantages of ensemble learning
2. Introduction to common algorithms for ensemble learning: principle and implementation
random forest
adaboost
GBDT
Xgboost
Stacking
3. Academic applications of ensemble learning algorithms
2. Advanced Feature Engineering Processing Technology (8 hours)
1. The importance of feature engineering
2. Common feature engineering processing techniques:
Feature selection
Feature construction
feature transformation
feature learning
3. Application of feature engineering in academic research
3. Neural Networks and Deep Learning (8 hours)
1. The introduction of neural networks, why is deep learning needed?
2. Introduction and application of common deep learning models:
Convolutional Neural Networks and Image Processing
Recurrent Neural Networks and Text Analysis
Multimodal Networks and Applications
3. The application of deep learning in academic research:
stock market forecast
credit risk assessment
Asset Pricing
audition
Course fee
Machine learning 30-hour online course: 4,000 yuan;
Machine Learning Advanced Summer Course: 4200 RMB
Provide electronic invoices , course start notices , and completion certificates
discount information
Buy two courses at the same time, you can get a 10% discount on all of them;
10% discount for veteran students;
Discount offers do not stack.
Registration consultation
