Ⅰ. Program Introduction

The 2021 Summer "Machine Learning +" online learning course of The Massachusetts Institute of Technology (MIT) is sponsored by the Department of Electrical Engineering and Computer Science (EECS),  Professors from MIT, Media Lab and MIT Sloan will deliver lectures. The course is guided by project-based Learning (PBL), combining classical theories, frontier applications, practical projects and other aspects. In addition to subject courses, it also includes modules such as project sharing and cloud Workshop for technology enterprises, enabling students to experience MIT teaching methods, research methods and the latest subject developments through online learning.

Ⅱ. School Introduction

The Massachusetts Institute of Technology (MIT) is a world-renowned private research university, known for its top engineering and computer science, with many top-notch laboratories. In 1959, the first artificial intelligence laboratory was established in the world, and it is one of the most leading academic palaces in artificial intelligence in the world.

 

Ⅲ. Time

26 July 2021-27 July 2021 (5 weeks)

After completing the project application, students can participate in pre-learning for free 6 weeks before the start of the project. The main content includes Python learning package and relevant basic course guidance, etc. The teaching assistants will follow the whole process of tutoring and answer questions. Pre-learning will begin in June, and students can adjust to the final exam schedule at no additional cost. Learning materials will be sent to students by email after application is completed.

 

Ⅳ. Program Course

The program has three options, and students can choose courses based on their major and interest.

Students can choose homework, group practice task and assessment of corresponding difficulty according to their professional knowledge base and interests (divided into two grades according to difficulty). The second level is more difficult in homework and project. We recommend students with honors colleges, special training programs and related disciplines to participate in the program. We will arrange project groups according to the situation of the enrolled students.

After passing the project assessment, students will receive an official study certificate and a report (divided into two grades according to the difficulty level), and students with excellent performance will have the opportunity to obtain a recommendation letter. During the program, there will also be sharing of topics such as well-known enterprises in the field of artificial intelligence and MIT students' learning/research experience.  Students who are interested in research and planning can also apply for a research assistant position in a relevant MIT laboratory/research institute after the project.

 

 

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 course is recommended for students majoring in management, economics, finance, mathematics, statistics and computer science and who are interested in the direction of the program.  The main content and application cases of the course include:

 

•        Introduction to Machine Learning

 

•        Supervised learning via Perceptron

 

•        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

 

•           1/2/ Quantitative investment in Statistical Measurement 1/2/

 

•            Introduction to Quantitative Investment  with Business Analysis

 

•            1/2 Application: Quantitative Investment with Business Analysis 1/2

 

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

 

 

 

➢           Deep Learning in Computer Vision

Inspired by neuroscience, deep learning simulates the cognitive and expression process of human brain and builds a logical hierarchical model of learning the internal implied relationship of data through function mapping from low-level signals to high-level features. Especially in the field of machine vision, deep learning has powerful visual information processing ability. This course is recommended for students who are interested in electronic information, computer science, automation, biomedicine and other related majors.  The main content and application cases of the course include:

 

•        Introduction to Machine Learning

 

•         Supervised learning via Perceptron

 

•         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)

 

 

 

➢            Deep Learning in Autonomous System

Deep learning and autonomous driving will focus on how to apply the basic theories of deep learning to the basic models and algorithms of autonomous driving. In view of the urgent needs of the contemporary society for the development of autonomous vehicles, research on the application of deep learning in autonomous vehicles is carried out. It can not only improve the accuracy of perception, but also strengthen the learning control.  This course is recommended for mechanical, transportation, instrumentation, automation, electronic information and other related majors and students who are interested in this program. The main content and application cases of the course include:

 

•          Introduction to ML

 

•          Supervised learning via Perceptron

 

•           Logistic Regression

 

•           Nonlinear features and Kernels

 

•            Regression

 

•            Neural Nets, Introduction

 

•            Neural Networks, Optimization

 

•            Convnets

 

•            EM: Unsupervised learning: clustering, mixture models EM

 

•             Recommender Systems

 

•              CNN architectures

 

•              Sequential image processing

 

•              Generative image modeling

 

•               Neural graphics and rendering

 

•               Mapping and Localization

 

•               Virtual SLAM for Self-Driving Vehicles

 

•               End to End Learning of Robotic Actuation

 

•                Deep Reinforcement Learning for Control

 

•                Deep Reinforcement Learning for Vehicle Motion Planning

 

•                Future of Human-Centered Autonomy

 

	Monday	Tuesday	Wednesday	Thursday	Friday	Saturday/Sunday
First Week	L1-5Recording & live broadcast +Q/A q&A
Second Week	L6-10Recording & live broadcast +Q/A q&A
Third Week	L11-15Recording & live broadcast +Q/A q&A					Topic Sharing
Fourth Week	L16-20Recording & live broadcast +Q/A q&A					Topic Sharing
Fifth Week	Q/A + Exam week +Team Project

 

Final schedule is subject to Project Syllabus

 

Ⅵ. Teaching Team

The teaching team includes professors, researchers and post-docs from MIT's EECS/Media Lab/ Sloan School of Management, all of whom have rich teaching experience and research project experience.  In addition, there will be a PhD/post-doctoral fellow from MIT as a teaching assistant to guide students throughout their study and answer questions。

 

 

 

✓             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

 

 

 

Ⅶ. Fees

Fee standard: $1530 / person (about 9900 yuan/person) (After completing the online course, you can get a full coupon for MIT's offline short-term exchange program in summer and winter, which can only be used by yourself）

 

 

Ⅷ. Application Requirements

1. Full-time undergraduates and postgraduates of BNUBS;
2. Good English listening and speaking skills;
3.  
4. Basic knowledge of Python programming is required (students without basic knowledge of Python can complete the Python self-study package during pre-learning under the guidance of the assistant).

 

Ⅸ. Application Method

 

Click apply link, fill in personal information to complete the application: https://jinshuju.net/f/bbkDcS

Deadline for application: May 30, 2021

* 4-6 weeks of pre-learning will be conducted by a teaching assistant upon completion of the program application

 

Ⅹ. Consult

For more information, please contact Teacher Shang, International Office of BNUBS, Tel: 58802691，Email: syh@bnu.edu.cn