Ⅰ. Project Introduction

MIT 2021 winter vacation "machine learning +" online learning course is lectured by MIT electrical engineering and computer science department (EECS,MIT), media laboratory (Media Lab) and Sloan school of management and other core laboratories.

The course is led by the practical project teaching method (Project-Based Learning, PBL), combined with the classical theory of the subject, the frontier application, the practical project and so on. In addition to the curriculum, it also includes topics sharing, science and technology enterprise cloud workshop and other modules, so that students can experience MIT teaching methods, research methods and the latest subject dynamics through the form of online learning.

 

 

Ⅱ. School Introduction

 

The Massachusetts Institute of Technology (MIT) is one of the world's leading private research university, known for its leading engineering and computer science laboratories, in 1959, the world's first Artificial Intelligence Laboratory was born, and it is the world's artificial intelligence in one of the most leading academic halls.

 

Ⅲ. Project Time

2021 18 Jan-21 Feb (2021 time)

The course includes video-recorded course, live-broadcast course, live-broadcast question-and-answer session, etc. . Students can arrange their own study time for the video-recorded course, and complete learning before the project assessment. The live broadcast course will be broadcast live through Zoom and other live broadcast platforms, which can be freely viewed in the recording platform playback. The live broadcast is usually scheduled between

 

Ⅳ. Project Curriculum

There are two optional directions for the project curriculum. Students can choose the course according to their professional and interest, and complete the corresponding practical project tasks. After the project assessment, the official learning certificate and grade report will be obtained. Excellent students will have the opportunity to get a recommendation letter. Students with scientific research interests and plans have the opportunity to apply for research assistants from the Laboratory/Research Institute of MIT after the project.

 

 

（1）Machine Learning in Business Analytics

Machine learning plays an increasingly prominent role in business analysis and decision-making process. Machine learning enables enterprises to accomplish process supervision, decision-making assistance, process optimization and predictive analysis more efficiently in the era of artificial intelligence.  The main content and application cases of this course include:

Introduction to Machine Learning

 

Supervised learning via Perceptrons

 

Logistic Regression

 

Nonlinear features and Kernels

 

Regression

 

Neural Nets, Introduction

 

Neural Networks, Optimization

 

EM Unsupervised learning: clustering, mixture models, EM

 

Recommender Systems

 

Machine Learning in Data Science

 

Machine Learning in Marketing

 

Machine Learning and Personalization – Static Setting

 

Machine Learning and Personalization – Dynamic Setting

 

Machine Learning and Personalization – Behavioral and Economic Insights

 

Machine Learning in Fin-Tech

 

Quantitative investment in Statistical Measurement 1/2/

 

Introduction to Quantitative Investment  with Business Analysis

 

Application: Quantitative Investment with Business Analysis 1/2

 

AI-Driven Stock Price Analysis-the rise of the quants 1/2

 

 

 

（2）Deep Learning and Its Applications

 

Deep Learning, inspired by neuroscience, simulates the cognitive and expressive processes of the human brain, and builds a logical hierarchical model of the implicit relationship within the learning data through the functional mapping of low-level signals to high-level features. Especially in the field of machine vision, deep learning has powerful visual information processing ability. The main content of this course and examples of its application include

Introduction to Machine Learning

 

Supervised learning via Perceptrons

 

Logistic Regression

 

Nonlinear features and Kernels

 

Regression

 

Neural Nets, Introduction

 

Neural Networks, Optimization

 

EM Unsupervised learning: clustering, mixture models, EM

 

Recommender Systems

 

Introduction to Deep Learning

 

Neural Networks and Convolutional Processing、

 

CNN Architectures (AlexNet, Resnet, etc.)

 

Vision with Sequences (Captioning, Video Processing, and Transformers)

 

Generative Image Modeling

 

Applications: Depth Estimation, Segmentation, Object Detection (YOLO, FasterRCNN)

 

Neural Rendering and Graphics

 

Interpretability and Uncertainty

 

Fairness and Bias of Vision Modelling

 

3D Reconstruction with Deep Networks (Models and Applications)

 

Ⅴ. Teaching Team

The teaching team includes professors, researchers and postdoctors from MIT eecs/media lab/ Sloan School of management. They all have rich teaching experience and scientific research project experience. In addition, there will be doctors / postdoctors from MIT as teaching assistants to guide students' learning and Q & A throughout the process

 

 

Prof. Hui CHEN

 

Professor of Finance at the MIT Sloan School of Management,

 

Research Associate at the National Bureau of Economic Research.

 

Teaching 15.450 Analytics of Finance, 15.457 Advanced Analytics of Finance

 

 

 

Prof. Suvrit Sra

 

Esther and Harold E. Edgerton Career Development Associate Professor of MIT EECS,

 

Core member of IDSS and LIDS, MIT,

 

Teaching 6.881 Optimization for Machine Learning, 6.867 Machine Learning

 

 

 

Prof. Shimon Kogan

 

Visiting Associate Professor of Finance at MIT Sloan School of Management

 

Teaching FinTech: Business, Finance, and Technology

 

 

 

Dr. Alexander Amini

 

PhD at MIT, in the Computer Science and Artificial Intelligence Laboratory (CSAIL),

 

Researcher, Distributed Robotics Laboratory, CSAIL, MIT

 

Teaching 6.S191 Introduction to Deep Learning

 

 

 

Dr. Roy Shilkrot

 

Research Scientist at Media Lab, MIT.

 

Teaching MAS.S60: Experiments in Deepfakes

 

6. Project expenses

Standard: 9900 yuan/person (After completing the online course, you can get 9,900 yuan MIT's cold and summer offline short -term exchange project deduction coupons, only to be used by me)

 VII. Application Requirement

Students, graduate students (including MBA);

 

2. Good English listening and speaking skills;

3. Must have a certain Python language programming foundation (students without Python foundation will be guided by teaching assistants to complete Python self-study package before the project).

VIII. Application method

Registration link: https://jinshuju.net/f/gagm4a

Application deadline: December 25th, 2020

IX. Project consultation

Teacher Shang, syh＠bnu．edu．cn, 588020