Machine Learning in Sichuan

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Machine Learning

Mianyang , Sichuan , China |

Part-Time / Offline Teaching

6000-8000 USD / Per Course

Air Ticket Visa Provided Commercial MedicalInsurance Accommodation Provided

Basic Info

Course Name: Machine Learning

Students: Undergraduate Students

Class Size: 80-100 students

Location: A public university in Mianyang City, Sichuan China

Teaching Period: 8 Weeks -10 Weeks

Contract: One Semester

Course Description

This course is designed for master's degree students in artificial intelligence, systematically constructing the theoretical system and practical framework of machine learning. It covers supervised learning (linear regression, SVM, decision tree, ensemble learning), unsupervised learning (clustering, dimensionality reduction, EM algorithm), and the fundamentals of deep learning (CNN, RNN, Transformer), with a focus on analyzing model principles, loss function construction and optimization algorithms (gradient descent, Adam). Combining Bayesian methods, regularization techniques and model evaluation strategies, core issues such as overfitting and bias-variance trade-off are explored. Through Python programming and the practice of PyTorch/TensorFlow frameworks, combined with real cases of CV, NLP and data mining, students' full-process capabilities from data preprocessing, model selection to deployment optimization are cultivated, laying a solid foundation for the cutting-edge research and industrial application of AI.

Job Highlights

1. Visa application fee reimbursement

2. Free round trip air tickets

3. Free accommodation

4. Medical Insurance

Job Responsibilities

1. Make a curriculum plan according to the requirements of the university, prepare lesson plans carefully, teach flexibly, and accomplish teaching tasks and goals efficiently;

2. Submit teaching documents regularly according to university's requirements based on the teaching progress, including course notes, slides, classroom exercises, and homework;

3. According to the university’s exam regulations, submit exam papers on time and assist the university in completing invigilation, grading, timely complete student grade registration and feedback reports.