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All Projects → Zhenye-Na → Machine Learning Uiuc

Zhenye-Na / Machine Learning Uiuc

🖥️ CS446: Machine Learning in Spring 2018, University of Illinois at Urbana-Champaign

Programming Languages

python
139335 projects - #7 most used programming language

Labels

deep-learningmachine-learningtensorflowreinforcement-learningdeep-reinforcement-learning

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CS446 Machine Learning

Open Source Love star this repo fork this repo HitCount

Table of Contents:

Course Information:

The goal of Machine Learning is to build computer systems that can adapt and learn from data. In this course we will cover three main areas:

  1. Discriminative models
  2. Generative models
  3. Reinforcement learning models

In particular we will cover the following:

  • Linear Regression
  • Logistic Regression
  • Support Vector Machines
  • Deep Nets
  • Structured Methods
  • Learning Theory
  • kMeans
  • Gaussian Mixtures
  • Expectation Maximization
  • Markov Decision Processes
  • Q-Learning

Pre-requisites:

Probability, Linear Algebra, and proficiency in Python.

Recommended Text:

  1. Machine Learning: A Probabilistic Perspective by Kevin Murphy
  2. Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
  3. Pattern Recognition and Machine Learning by Christopher Bishop
  4. Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and Nir Friedman

Instructors:

  • Alexander Schwing, Website [Link]
  • Matus Telgarsky, Website [Link]

Assignments

  • Assignment 1: Introduction + Python — Design by Colin, Review by Yucheng
  • Assignment 2: Linear Regression — Design by Raymond, Review by Jyoti
  • Assignment 3: Binary Classification — Design by Youjie, Review by Jyoti
  • Assignment 4: Support Vector Machine — Design by Raymond, Review by Ishan
  • Assignment 5: Multiclass Classification — Design by Yucheng, Review by Safa
  • Assignment 6: Deep Neural Networks — Design by Safa, Review by Yuan-Ting
  • Assignment 7: Structured Prediction — Design by Colin, Review by Yucheng
  • Assignment 8: k-Means — Design by Jyoti, Review by Youjie
  • Assignment 9: Gaussian Mixture Models — Design by Ishan, Review by Colin
  • Assignment 10: Variational Autoencoder — Design by Yuan-Ting, Review by Raymond
  • Assignment 11: Generative Adverserial Network — Design by Ishan, Review by Yuan-Ting
  • Assignment 12: Q-learning — Design by Safa, Review by Youjie

Announcement:

All copyrights reserved © CS446 Instructors & TAs

  • Raymond Yeh, Website [Link]
  • Colin Graber
  • Safa Messaoud
  • Yuan Ting Hu
  • Ishan Deshpande
  • Jyoti Aneja
  • Youjie Li
  • Yucheng Chen
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].