For machine learning algorithm engineers, please give a study outline

专业户

For machine learning algorithm engineers, please give a study outline [Copy link]

543

The following is a study outline suitable for getting started as a machine learning algorithm engineer:

1. Basic mathematics knowledge

Linear algebra: matrices, vectors, matrix operations, eigenvalue decomposition, singular value decomposition, etc.
Calculus: derivatives, partial derivatives, gradients, integrals, etc.
Probability theory and statistics: probability distribution, expectation, variance, maximum likelihood estimation, Bayesian inference, etc.

2. Machine Learning Basics

Basic concepts of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Common machine learning algorithms: linear regression, logistic regression, decision tree, random forest, support vector machine, clustering algorithm, etc.
Basics of deep learning: artificial neural networks, convolutional neural networks, recurrent neural networks, etc.

3. Data preprocessing and feature engineering

Data cleaning: missing value processing, outlier processing, duplicate value processing, etc.
Feature selection and transformation: feature selection methods, feature transformation methods, feature construction, etc.

4. Model evaluation and tuning

Evaluation indicators: accuracy, precision, recall, F1-score, ROC curve, AUC, etc.
Cross-validation: k-fold cross-validation, leave-one-out cross-validation, etc.
Hyperparameter tuning: grid search, random search, Bayesian optimization, etc.

5. Model deployment and optimization

Model deployment: containerization, service-oriented, distributed deployment, etc.
Model optimization: model pruning, model compression, quantization, etc.

6. Practical Projects

Complete some machine learning projects based on real data sets, such as house price prediction, image classification, text classification, etc.

7. References and Resources

Classic textbooks such as "Machine Learning" (Zhou Zhihua) and "Statistical Learning Methods" (Li Hang).
Online courses and tutorials, such as machine learning courses offered by Coursera, edX, etc.
Official documentation and sample code for open source machine learning frameworks.

By following this outline, you can gradually build up the skills and knowledge required by a machine learning algorithm engineer to support the design, development, and optimization of machine learning models in real projects.

黄金时代

The following is a study outline for an introductory course on numerical optimization for machine learning that is suitable for electronics veterans:

Understand the basics of numerical optimization :
- Learn the basic concepts and principles of numerical optimization, including the definition of optimization problems, objective functions, and constraints.
- Understand the importance and application scenarios of numerical optimization in machine learning.
Master optimization algorithms :
- Learn common numerical optimization algorithms, such as gradient descent, Newton's method, quasi-Newton's method, and conjugate gradient method.
- Understand the advantages, disadvantages and applicable scope of various optimization algorithms, and choose appropriate algorithms to solve different types of optimization problems.
Learn optimization tools and libraries :
- Master the use of optimization tools and libraries, such as SciPy, CVXPY, and TensorFlow.
- Learn how to use these tools and libraries to implement optimization algorithms and apply them in machine learning.
Optimization problem modeling :
- Learn how to model practical machine learning problems as optimization problems, including model parameter optimization and loss function minimization.
- Explore applications of optimization problems in machine learning, such as model training, hyperparameter tuning, and feature selection.
Practical projects :
- Choose some optimization projects or exercises related to the electronics field, such as circuit layout optimization, signal processing parameter tuning, etc.
- Use the learned numerical optimization knowledge and tools to complete the implementation and evaluation of the project, and deepen the understanding and application of numerical optimization in machine learning.
Continuous learning and practice :
- Continue to learn the latest progress and research results in the field of numerical optimization and machine learning, and pay attention to new algorithms and technologies.
- Participate in relevant training courses, seminars and community activities, communicate and share experiences with peers, and continuously improve the application capabilities of numerical optimization in machine learning.

Through the above learning outline, you can gradually master the basic knowledge of numerical optimization required in machine learning and lay a solid foundation for applying machine learning technology in the electronics field.

up2milk

Here is an introductory study outline for numerical optimization in machine learning for electronic engineers:

1. Basics of Numerical Optimization

Understand the basic concepts and mathematical forms of optimization problems
Learn common optimization objective functions and constraint representation methods
Master the methods of solving optimization problems, including analytical solutions and numerical solutions

2. Gradient Descent

Understand the basic principles and mathematical derivation of the gradient descent method
Learn about variants of gradient descent, such as batch gradient descent, stochastic gradient descent, and mini-batch gradient descent
Master the implementation steps and parameter adjustment techniques of the gradient descent method, including the selection of learning rate and analysis of convergence.

3. Newton's method and quasi-Newton's method

Learn the basic principles and mathematical derivations of Newton's method and quasi-Newton's method
Understand the advantages, disadvantages and applicable conditions of Newton method and quasi-Newton method in optimization problems
Master the implementation methods and parameter adjustment techniques of Newton method and quasi-Newton method, including the calculation and update strategy of Hessian matrix, etc.

4. Global Optimization Method

Understand the basic ideas and solution strategies of global optimization methods
Learn common global optimization algorithms, such as genetic algorithm, simulated annealing algorithm and particle swarm optimization algorithm, etc.
Master the implementation steps and parameter settings of global optimization methods, including the selection of population size and the evaluation of convergence.

5. Stochastic Optimization Methods

Understand the basic principles and random nature of stochastic optimization methods
Learn common stochastic optimization algorithms such as stochastic gradient descent and random search algorithms
Master the implementation techniques and parameter adjustment strategies of random optimization methods, including the selection of sampling methods and the control of the number of iterations.

6. Practical projects and case analysis

Complete programming implementation and algorithm debugging of relevant numerical optimization algorithms
Participate in the practice and case analysis of machine learning projects, and apply the learned numerical optimization methods to solve practical problems

7. Continuous learning and expansion

Learn advanced aspects of numerical optimization theory, such as convergence proofs and complexity analysis
Continuously practice and try new numerical optimization algorithms and techniques to maintain enthusiasm and motivation for learning

The above is an introductory learning outline for numerical optimization in machine learning for electronic engineers, covering the basics of numerical optimization, gradient descent method, Newton method and quasi-Newton method, global optimization methods, stochastic optimization methods, etc.