Advanced Machine Learning Specialization

开始时间: 01/29/2018 持续时间: Unknown

所在平台: Coursera专项课程

课程类别: 计算机科学

大学或机构: CourseraNew

   

课程主页: https://www.coursera.org/specializations/aml

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课程详情

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

课程大纲

7 courses

Introduction to Deep Learning
Upcoming session: Jan 29
The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of st

How to Win a Data Science Competition: Learn from Top Kagglers
Upcoming session: Jan 29
If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains

Bayesian Methods for Machine Learning
Upcoming session: Jan 29
Bayesian methods are used in lots of fields: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also all

Practical Reinforcement Learning
Starts February 2018
The goal of «Intro to Reinforcement learning» is in its name: introduce students to reinforcement learning – the prominent area of modern research in artificial intelligence. The reinforcement learning differs much from both supervised and unsupervised learning and is more about how humans learn in reality. Students will learn from this course both theoretical core and recent practical RL methods. Most importantly, they will learn how to apply such methods to practical problems. In six weeks students will be guided through the basics of Reinforcement Learning (RL): we will talk about essential theory of RL, value-based methods (such as SARSA and Q-learning), policy based algorithms and methods, designed to solve the optimal exploration problem. In addition to algorithms and theory, during the course we will also present useful practical tips and tricks, needed for learning stabilization, and study how to apply the methods to large scale problems with deep neural networks.

Deep Learning in Computer Vision
Starts February 2018
Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and oftenly demonstrated in movies and TV-shows example of computer vision and AI.

Natural Language Processing
Starts February 2018
This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection. Throughout the lectures, we will aim at finding a balance between traditional and deep learning techniques in NLP and cover them in parallel. For example, we will discuss word alignment models in machine translation and see how similar it is to attention mechanism in encoder-decoder neural networks. Core techniques are not treated as black boxes. On the contrary, you will get in-depth understanding of what’s happening inside. To succeed in that, we expect your familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks. Some materials are based on one-month-old papers and introduce you to the very state-of-the-art in NLP research.

Addressing Large Hadron Collider Challenges by Machine Learning
Starts March 2018
The Large Hadron Collider (LHC) is the largest data generation machine for the time being. It doesn’t produce the big data, the data is gigantic. Just one of the four experiments generates thousands gigabytes per second. The intensity of data flow is only going to be increased over the time. So the data processing techniques have to be quite sophisticated and unique. In this course we’ll introduce students into the main concepts of the Physics behind those data flow so the main puzzles of the Universe Physicists are seeking answers for will be much more transparent. Of course we will scrutinize the major stages of the data processing pipelines, and focus on the role of the Machine Learning techniques for such tasks as track pattern recognition, particle identification, online real-time processing (triggers) and search for very rare decays. The assignments of this course will give you opportunity to apply your skills in the search for the New Physics using advanced data analysis techniques. Upon the completion of the course you will understand both the principles of the Experimental Physics and Machine Learning much better.

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Deep Learning Specialization on Coursera

课程简介

由俄罗斯国立高等经济学院和Yandex联合推出的高级机器学习专项课程系列(Advanced Machine Learning Specialization),该系列授课语言为英语,包括深度学习,Kaggle数据科学竞赛,机器学习中的贝叶斯方法,强化学习,计算机视觉,自然语言处理等7门子课程,截止目前3门课程已开,感兴趣的同学可以关注: Deep Dive Into The Modern AI Techniques-You will teach computer to see, draw, read, talk, play games and solve industry problems.

课程标签

机器学习 高级机器学习 高级机器学习专项课程 深度学习 强化学习 计算机视觉 自然语言处理 Kaggle 数据科学 数据科学竞赛 Kaggle竞赛 贝叶斯方法

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