CS420 Machine Learning 2020 

Weinan Zhang, Assistant Professor

Zhiyuan College
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.

Notice

Our first lecture starts on 2 Mar 2020!

The internet lectures of CS420 2020 will be broadcast on Bilibili. The live broadcast time: 12:55-15:40 every Monday, from 2 Mar to 8 Jun. 2020. Live broadcast link: https://live.bilibili.com/21838900.

Slides (2019 Version)

Due to the Internet lecturing scheme in 2020, we will not use English slides this year. Thus here we keep the English slides of 2019 for your reference.


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Lecture 1: Introduction to Machine Learning
Introduction about AI, machine learning, data science and various ML applications
Feb. 25, 2019

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Home Reading 1A: Mathematics for Machine Learning
Mathematic fundamentals for machine learning, including Algebra, Probabilistic, Statistics and Optimization etc.
Feb. 25, 2019

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Lecture 2: Linear Models for Supervised Learning
Discriminative/generative models, linear regression, logistic regression
Mar. 4, 2019

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Lecture 3: Support Vector Machine and Kernel Methods
Maximizing Margin Classification, SVMs, Convex Optimization, Kernel Tricks
Mar. 11, 2019

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Lecture 4: Neural Networks
Perceptron, Multilayer Perceptron, Backprop, Deep Learning
Mar. 18, 2019

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Lecture 5: Tree Models
Decision Trees, ID3, CART
Mar. 25, 2019

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Lecture 6: Ensemble and Boosting Algorithms
Ensemble Methods, Bagging, Boosting
Apr. 1, 2019

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Lecture 7: Ranking and Filtering
Learning to Rank, Collaborative Filtering, Matrix Factorization
Apr. 8, 2019

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Lecture 8: Probabilistic Graphical Models
Bayes Network, Markov Network, Conditional Independence, Message Passing
Apr. 15-22, 2019

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Lecture 9: Unsupervised Learning
PCA, Mixture Gaussians, EM Methods, Auto-encoders, GANs
Apr. 29, 2019

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Lecture 10: Learning Theory and Model Selection
PAC Learning Theory, VC Demension, Bias Variance Decomposition, Feature Selection
May 6, 2019

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Lecture 11: Introduction to Reinforcement Learning
Reinforcement Learning, Markov Decision Process, Dynamic Programming, Model-free RL
May. 13, 2019

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Lecture 12: Approximation Methods in RL
Value Approximation Methods, Policy Gradients, Actor Critic
May. 20, 2019

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Lecture 13: Deep Reinforcement Learning
Deep Q-Networks, Trust Region Policy Gradient, Deep Deterministic PG
May. 27, 2019

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Lecture 14: Multi-Agent Reinforcement Learning
Stochastic Games, Nash Q-Learning, Mean-Field Q-Learning
May. 27, 2019

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Lecture 15: Transfer Learning
Transfer Learning, Domain Adaptation, Importance Sampling, Parameter Sharing
Jun. 3, 2019

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Lecture 16: Meta Learning
Meta Learning, Learning to Learn, MAML, RNN4SGD, RL4SGD
Jun. 3, 2019

Related Readings

Teaching Assistants (Spring 2020)


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Yating Luo, ACM17 student, research intern in ApexLab
Research on natural language processing.
Email: 13531665402 [AT] sjtu.edu.cn

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Weizhe Chen, ACM17 student, research intern in ApexLab
Research on multi-agent reinforcement learning and AI for social good.
Email: chenweizhe [AT] sjtu.edu.cn

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Yaxing Cai, ACM17 student, research intern in ApexLab
Research on multi-agent reinforcement learning and intelligent city.
Email: caiyaxing [AT] sjtu.edu.cn

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Wentao Zhang, ACM17 student, research intern in ApexLab
Research on natural language processing and data mining.
Email: zwt1999 [AT] sjtu.edu.cn

Past Courses


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CS420 Machine Learning 2018
Compared to CS420 2018, CS420 2019 will be more comprehensive on multi-agent reinforcement learning and meta-learning. The course works will be more practical and close to industry.

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CS420 Machine Learning 2017
Compared to CS420 2017, CS420 2018 adds more materials on (multi-agent) reinforcement learning.

News


Feb. 1, 2020
Yating Luo, Weizhe Chen, Yaxing Cai, Wentao Zhang appointed as the teaching assitants of CS420 2020.