CS420 Machine Learning
Shanghai Jiao Tong University
Email: wnzhang [AT] sjtu.edu.cn
Machine learning is the science of training machines with non-explicit programming based on a dataset to get them work on intelligent tasks. Machine learning has obtained fast development during the last two decades and now plays an important role in various aspects of our daily life, such as weather forecasting, e-commerce personalized recommendation, news categorization, face recognition, speech QA, self-driving, home robots, and medical expert system etc. Particularly, in March 2016, Google's AlphaGo beat Lee Se-dol on Game of Go with the score 4-1, which indicates the arrival of the new artificial (general) intelligence era with machine learning based on big data.
This course provides a comprehensive introduction of the fundamental problems and methodologies of machine learning, including supervised/unsupervised learning (covering most prediction applications, e.g., recommender systems, image recognition and webpage ranking etc.) and reinforcement learning (covering all decision-making applications, e.g., playing Go, self-driving, ad bidding and smart stock picking etc.). Additionally, the coursework includes hands-on tasks, in which the students are required to design machine learning programs to accomplish several intelligence tasks, and are highly encouraged to further improve the machine performance via trying different models and refining the code implementation.
For students formally taking this course, please send your [name, student ID, email address] to the TA Han Cai hcai[A.T.]apex.sjtu.edu.cn for subscription.
|Course work 1: Text Classification
To create a classification model for recommending selected articles.
Mar. 6, 2017 - Apr. 16, 2017.
|Lecture 1: Introduction to Machine Learning
Introduction about AI, machine learning, data science and various ML applications
Feb. 20, 2017
|Lecture 2: Linear Models for Supervised Learning
Linear regression, logistic regression, evaluation metrics
Feb. 27, 2017
|Lecture 3: SVM and Kernel Methods
Support Vector Machines, Convex Optimization, Kernel Methods
Mar. 6, 2017
|Lecture 4: Neural Networks
Perceptron, Neural Networks, Deep Learning
Mar. 13, 2017
|Lecture 5: Decision Tree Models
Decision Trees, ID3, CART
Mar. 20, 2017
- Matrix Cookbook: fundamentals of matrix calculations.
- Stanford CS229 Machine Learning course by Prof. Andrew Ng.
- Convex Optimization by Prof. Stephen Boyd.
|Kan Ren, Ph.D. student in ApexLab
Research on data mining, computational advertising and reinforcement learning
Email: kren [AT] apex.sjtu.edu.cn
|Han Cai, Master student in ApexLab
Research on reinforcement learning
Email: hcai [AT] apex.sjtu.edu.cn
Mar. 6, 2017
Feb. 26, 2017
Feb. 20, 2017