Coursera完成課程列表

04-17

這篇文章由用戶 @朱里整理，用於記錄在Coursera上完成的課程列表。

// This is a memo composed by user @朱里 to keep track of a list of finished courses on Coursera.

由於Coursera學習記錄的總篇幅超出了知乎的長度上限，我只能把課程列表單獨拿出來開一篇文章。

// Due to the length limit set by the Zhihu editor, I have to put the list content here as a separate article, instead of in the parent article Coursera Learning Notes.

距離這篇課程列表超出長度上限，估計還有段時間。

// Im sure it will take quite some time before this one exceeds the length limit too.

以下部分為我的刷課記錄：

// The following is a list of my finished courses:

列表部分會和zhuli19901106/coursera-learning的README.md同步更新。

// The list will be in sync with the README.md of zhuli19901106/coursera-learning.

點評部分的中英文不一致，因為英文是在提交Github時寫的，中文是在更新文章時寫的。

// There can be discrepancies in the review part, as the review in English is written when pushing commits to Github, while the Chinese part is written when updating this article.

「時間」指的是「完成時間」。

// "Time" refers to "date of completion".

Algorithms Part I

Organization: Princeton University
URL: Algorithms, Part I | Coursera
Time: April 2, 2017
Grade: 98.6/100
Topic: data structure, algorithm, Java
Review: The lecture speed was a bit too slow, so I had to go at between 1.25x and 1.5x speed to save time. Everything is really well explained, making this course very friendly even for fresh beginners. Actually I signed up for this just to do a little practice in Java programming. Considering the ammount of work devoted to the programming assignments, it was a wise decision to join up, well worth the time and efforts.

演算法1

機構：普林斯頓大學
鏈接：演算法1
時間：2017年4月2日
成績：98.6/100
主題：數據結構、演算法、Java編程
點評：轉CS必修課之一，對於CS科班的學生，作為基礎課複習也是很不錯的。免費課程，業界良心。

Machine Learning

Organization: Stanford University
URL: Machine Learning | Coursera
Time: April 29, 2017
Grade: 100/100
Topic: machine learning, MATLAB
Review: This is one of the "founding courses" of Coursera, thus it is supposed to be easy and interesting (otherwise people wouldve been scared off in the first place). So it is vital, for every programming assignment, that you try to read and understand the 99% of codes already written for you. The 1% for you to finish is really the trivial part. Otherwise therell be no gain at all. Also its fresh experience for those who get to think in a vectorized manner for the first time.

機器學習

機構：斯坦福大學
鏈接：機器學習
時間：2017年4月29日
成績：100/100
主題：機器學習、MATLAB
點評：Coursera創立時的招牌課之一，零門檻免費課，為了讓隔壁看自行車的王大爺都聽得懂，難度設定得極其簡單。業界良心。

Functional Programming Principles in Scala

Organization: école Polytechnique Fédérale de Lausanne
URL: Functional Programming Principles in Scala | Coursera
Time: May 10, 2017
Grade: 100/100
Topic: functional programming, Scala
Review: A course to learn Scala as well as functional programming. Im a freshman for FP, so the programming assignments did give me a little challenge. I guess when one gots so fixed in the mindsets of imperative and objective programming, the adaptation to FP can be rough. Im gonna finish the whole specialization, as theyre all free for now : )

Scala函數式編程

機構：洛桑聯邦理工學院
鏈接：Scala函數式編程
時間：2017年5月10日
成績：100/100
主題：函數式編程、Scala編程
點評：免費課，由Scala發明者授課，很不錯。

Functional Program Design in Scala

Organization: école Polytechnique Fédérale de Lausanne
URL: Functional Program Design in Scala | Coursera
Time: May 25, 2017
Grade: 100/100
Topic: functional reactive programming, Scala
Review: Relatively short.

Scala函數式程序設計

機構：洛桑聯邦理工學院
鏈接：Scala函數式程序設計
時間：2017年5月25日
成績：100/100
主題：函數響應式編程、Scala編程
點評：免費課，由Scala發明者授課，上一門課的後續，老鐵沒毛病。

Discrete Optimization

Organization: University of Melbourne
URL: Discrete Optimization | Coursera
Time: July 5, 2017
Grade: 93/100
Topic: combinatorial optimization, meta-heuristics, randomization
Review: Very challenging course which requires solid programming skill and lots of paper reading. Given the hands-on experience related to optimization techniques, its totally worth all the time and efforts.

離散優化

機構：墨爾本大學
鏈接：離散優化
時間：2017年7月5日
成績：93/100
主題：組合優化、啟發式演算法、隨機化演算法
點評：免費課，也是Coursera上難得的沒水分的課。課程視頻和學習資料非常詳實，編程作業非常難（如果你想拿滿分的話）。除了線性規劃、整數規劃之外，我為了刷這門課，查了十幾篇運籌學的論文，寫了幾千行的啟發式演算法代碼。終於成功避免了用Gurobi之類的優化器暴力求解，純粹靠啟發式演算法混過了絕大多數測試樣例，混了個93分。對NPC問題感興趣的同學，可以去挑戰下幾萬個點的TSP問題。你要能算出最優解，我喊你祖宗。話說我差點就放棄了，最後還是堅持下來了。

Parallel Programming

Organization: école Polytechnique Fédérale de Lausanne
URL: Parallel Programming | Coursera
Time: July 18, 2017
Grade: 100/100
Topic: scala programming, parallel computing, functional programming
Review: Third course of the specialization, relatively short and easy. The last pogramming assignment is fun, which is an N-body simulation problem.

並行編程

機構：洛桑聯邦理工學院
鏈接：並行編程
時間：2017年7月18日
成績：100/100
主題：Scala編程、並行編程、函數式編程
點評：免費課，水過。課程作業很有意思，是Barnes-Hut多體模擬。這是天文學計算的常見任務，對並行優化的要求很高。雖然這個課程作業里的模型不嚴謹，計算方式存在數值誤差累積，而且沒有考慮極短距離內引力助推效應的影響，導致模擬出來的效果與實際效果相去甚遠，但作為練習還是相當好玩的。

Game Theory

Organization: Stanford University, The University of British Columbia
URL: Game Theory | Coursera
Time: August 02, 2017
Grade: 100/100
Topic: game theory
Review: Introduction to introduction to game theory, all quiz no code.

博弈論

機構：斯坦福大學，英屬哥倫比亞大學
鏈接：博弈論
時間：2017年8月2日
成績：100/100
主題：博弈論
點評：免費課，水過。對於博弈論的入門介紹，僅限於小規模的理論分析和介紹，沒有演算法、編程層面的東西。

Big Data Analysis with Scala and Spark

Organization: école Polytechnique Fédérale de Lausanne
URL: Big Data Analysis with Scala and Spark | Coursera
Time: August 12, 2017
Grade: 100/100
Topic: scala programming, parallel computing, spark programming
Review: Fourth course of the specialization, relatively short and easy. The programming assignments took me a whole lot of time putting the APIs right. Its something you have to go through when learning a computation framework, no way around. Besides, the lecturer talks rather fast, with 1.5x play speed and no caption, I had the opportunity to practice my listening skill, thats the real fun.

Scala與Spark大數據分析

機構：洛桑聯邦理工學院
鏈接：Scala與Spark大數據分析
時間：2017年8月12日
成績：100/100
主題：Scala編程、並行編程、Spark編程
點評：免費課，水過。這門課多數時候在倒騰API，比較工程導向。老師講課語速很快，所以乾脆當成聽力訓練來搞。

Introduction to Data Science in Python

Organization: University of Michigan
URL: Introduction to Data Science in Python | Coursera
Time: August 30, 2017
Grade: 100/100
Topic: python programming, pandas, data science
Review: An introductory course in ipython and pandas. The interactive notebook called "Jupyter" has nice user experience. If youre looking to learn some pandas programming, try it out.

Python數據科學入門

機構：密歇根大學
鏈接：Python數據科學入門
時間：2017年8月30日
成績：100/100
主題：Python編程、Pandas編程、數據科學
點評：免費課，水過。Pandas入門課，第一次接觸Jupyter Notebook，好評。

Functional Programming in Scala Capstone

Organization: école Polytechnique Fédérale de Lausanne
URL: Functional Programming in Scala Capstone | Coursera
Time: September 3, 2017
Grade: 100/100
Topic: scala programming, parallel computing, data visualization, spark programming
Review: Fifth course of the specialization, a step-by-step guide to a full scale project. The programming is challenging, while not at maths and algorithm, but at parallel programming, memorization, functional programming, all sorts of tweaking to make your code faster and tighter. The grader has a pretty tight memory limit of 1.5GB, which turned out to be a real headache, for you can experience failures randomly, making the programming assignment unnecessarily much harder. Still, every bit of effort pays off. Try it and see for yourself.

Scala函數式編程綜合

機構：洛桑聯邦理工學院
鏈接：Scala函數式編程綜合
時間：2017年9月3日
成績：100/100
主題：Scala編程、並行編程、數據可視化、Spark編程
點評：免費課，不算太水。這是整個Scala系列的最後一門，要完成一個地圖可視化的作業，因為內存卡得非常緊，所以對於演算法、系統調優的要求還是有點高的，有一定挑戰性。

Microeconomics Principles

Organization: University of Illinois at Urbana-Champaign
URL: Microeconomics Principles | Coursera
Time: September 23, 2017
Grade: 100/100
Topic: microeconomics
Review: An introductory course in microeconomics. I guess this is course 101 for economics major.

微觀經濟學原理

機構：伊利諾基大學香檳分校
鏈接：微觀經濟學原理
時間：2017年9月23日
成績：100/100
主題：微觀經濟學
點評：免費課，經濟學101。

Model Thinking

Organization: University of Michigan
URL: Model Thinking | Coursera
Time: October 13, 2017
Grade: 100/100
Topic: social science
Review: An introductory course in social sciences. Its totally for high school students and undergraduate freshmen, with no rigorous math or hands-on case study projects. I guess Im too old for this.

建模思維

機構：密歇根大學
鏈接：建模思維
時間：2017年10月13日
成績：100/100
主題：社會科學
點評：免費課，內容很廣泛也很吸引人，但由於是文科課程，所有沒有數學推導。這門課其實是給高中生或者大一學生聽的，要是早年學的話會更合適。

Introduction to Programming with MATLAB

Organization: Vanderbilt University
URL: Introduction to Programming with MATLAB | Coursera
Time: October 18, 2017
Grade: 100/100
Topic: matlab programming
Review: An introductory course in matlab programming. I skipped the videos and went for the programming assignments directly. Its just some exercises to avoid getting all rusty.

MATLAB入門

機構：范德堡大學
鏈接：MATLAB入門
時間：2017年10月18日
成績：100/100
主題：MATLAB編程
點評：免費課，水過。

Applied Machine Learning in Python

Organization: University of Michigan
URL: Applied Machine Learning in Python | Coursera
Time: October 23, 2017
Grade: 100/100
Topic: machine learning, scikit-learn, pandas, numpy
Review: A very well-designed course, teach you to do machine learning by calling all sorts of APIs. Actually, for small to middle-sized datasets, I think this kind of approach is quite handy, or shall we say, "lightweight". For extremely large datasets, small samples can be analyzed with toolkits like this to help make some sense, before we embark on deep learning and system-level optimizations.

應用機器學習

機構：密歇根大學
鏈接：應用機器學習
時間：2017年10月23日
成績：100/100
主題：機器學習、Scikit-Learn、Pandas、Numpy
點評：免費課，水過。課程安排非常貼心，示例充分，作業難度合理。業界良心。

Applied Text Mining in Python

Organization: University of Michigan
URL: Applied Text Mining in Python | Coursera
Time: October 31, 2017
Grade: 100/100
Topic: text mining, nltk, gensim
Review: A brief introduction on text mining, with a few exercises in python.

應用文本挖掘

機構：密歇根大學
鏈接：應用文本挖掘
時間：2017年10月31日
成績：100/100
主題：文本挖掘、NLTK、Gensim
點評：免費課，水過。關於文本挖掘的一些傳統玩法，學習一個。

Bayesian Statistics: From Concept to Data Analysis

Organization: University of California, Santa Cruz
URL: Bayesian Statistics: From Concept to Data Analysis | Coursera
Time: November 26, 2017
Grade: 100/100
Topic: bayesian statistics, inferential statistics
Review: I took this course for some background knowledge on statistics, as a prerequisite for Probabilistic Graphical Models.

貝葉斯統計：從概念到數據分析

機構：加州大學聖克魯茲分校
鏈接：應用文本挖掘
時間：2017年11月26日
成績：100/100
主題：貝葉斯統計、統計推斷
點評：免費課，水過。作為學習概率圖模型之前的必知必會內容。

Algorithms Part II

Organization: Princeton University
URL: Algorithms, Part II | Coursera
Time: December 30, 2017
Grade: 100/100
Topic: data structure, algorithm, Java
Review: After being gone for so long, this course is finally back. I dont really expect to learn anything new from it, just for old times sake. The quizzes are gone, replaced by optional interview problems. The programming assignments are also much easier. If youre new to Computer Science, this is one of the courses you cant miss.

演算法2

機構：普林斯頓大學
鏈接：演算法2
時間：2017年12月30日
成績：100/100
主題：數據結構、演算法、Java編程
點評：免費課，水過。演算法1和演算法2作為轉CS的必修課，兩兄弟的命可真是不一樣吶。這門課被Coursera官方雪藏了一年多，最近才重新開課。對我而言雖然沒有什麼收穫，單純為了情懷也要把它刷掉。畢竟普林斯頓，畢竟修橋補路，畢竟業界良心。畢竟不像某些成功人士，整個辣子雞丁的水平還有臉開live滋滋滋收錢。

Probabilistic Graphical Models 1: Representation

Organization: Stanford University
URL: Probabilistic Graphical Models 1: Representation | Coursera
Time: January 4, 2018
Grade: 100/100
Topic: bayesian inference, markov model, matlab
Review: This is the first course of the PGM series, which teaches you some basics of Bayesian inference, Markov network, factor graphs, etc. Its gonna be the building blocks of the bigger picture. If youre not quite familiar with algebra, calculus and probability theory, youre gonna have a hard time doing this. Also, this course is created in 2012, when Python hant risen to power, so youll have to make do with MATLAB. The programming assignments are about 50% reading comprehension, 40% researching and 10% coding. Make sure you take the time to do it by yourself. Cheating only takes ten minutes, and youll gain nothing from it.

概率圖模型1：表示

機構：斯坦福大學
鏈接：概率圖模型1：表示
時間：2018年1月4日
成績：100/100
主題：貝葉斯統計、馬爾科夫模型、MATLAB編程
點評：付費系列課，一點都不水。這門課是Coursera創立時的另一門招牌課，由創始人Daphne Koller授課。實際上這門課的核心在於建立因子圖的思想，你要把聯合分布、條件分布、邊際分布掰扯明白，並且能用程序表述清楚。編程作業里需要動手的部分很少，主要是理解從貝葉斯統計到概率圖，從概率圖到代碼的思維轉化。實際上，我學完第一門課就出去面試了，後兩門課到現在還沒學。更囧的是，後面那些還沒學的東西，我已經在工作中開始用了。

Neural Networks and Deep Learning

Organization: deeplearning.ai
URL: Neural Networks and Deep Learning | Coursera
Time: January 7, 2018
Grade: 100/100
Topic: deep learning, neural network, python
Review: This is the first course of the deep learning specialization by Professor Andrew Ng. Its explicitly made extremely easy because they wish to let AI and Deep learning be known to the general public, not just math/CS/stats professionals. The course is a brief introduction on basic feedforward neural network. If youre a CS major, youre supposed to be able to finish this course within 3 days. Still, the interviews with several leading figures is the greatest part of this course. It is the "sense" from those academic masters thats the most valuable part, which we should try to perceive and follow. The programming assignments are organized as step-by-step tutorials, which take on average within 2 hours to finish.

神經網路與深度學習

機構：deeplearning.ai
鏈接：神經網路與深度學習
時間：2018年1月7日
成績：100/100
主題：深度學習、神經網路、Python編程
點評：吳恩達老師的深度學習系列課，付費。這個系列的課程，為了降低大伙兒的學習門檻，被特意設置得極其簡單。個人覺得這很好，有助於向大眾普及AI知識。基本上，按照Jupyter Notebook里的一步步指導就能完成。

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Organization: deeplearning.ai
URL: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization | Coursera
Time: January 11, 2018
Grade: 100/100
Topic: deep learning, alchemy, python
Review: This is the second course of the deep learning specialization by Professor Andrew Ng. Still, extremely well organized, and easy.

深度神經網路調優：走近玄學

機構：deeplearning.ai
鏈接：深度神經網路調優：走近玄學
時間：2018年1月11日
成績：100/100
主題：深度學習、鍊金術、Python編程
點評：吳恩達老師的深度學習系列課，付費。讓我們走近玄學。

Structuring Machine Learning Projects

Organization: deeplearning.ai
URL: Structuring Machine Learning Projects | Coursera
Time: January 12, 2018
Grade: 100/100
Topic: deep learning, alchemy
Review: This is the third course of the deep learning specialization by Professor Andrew Ng. Its a two-week lecture on the techniques, rules and inspirations on the strategies to appply when working on deep learning projects. Its basically about rules of thumbs, so dont try to obey everything to the letter and expect things to work like wonder if you do. Think about it, learn from it, reflect upon it. Still, the most valuable part is always the interview with key figures from academia and industry.

構建機器學習項目

機構：deeplearning.ai
鏈接：構建機器學習項目
時間：2018年1月12日
成績：100/100
主題：深度學習、鍊金術
點評：吳恩達老師的深度學習系列課，付費。老司機教你開車，不講微操和走位，只講意識。

Convolutional Neural Networks

Organization: deeplearning.ai
URL: Convolutional Neural Networks | Coursera
Time: January 21, 2018
Grade: 100/100
Topic: deep learning, alchemy
Review: This is the fourth course of the deep learning specialization by Professor Andrew Ng. Its about one of hottest catchphrases, CNN. Convolutional neural network is indeed powerful, in that its much more effecient and flexible than old-school MLP. Things also begin to get really misty from this point, as you see one magical model after another, without getting any sense where the hell is the explainability. If theres anything thats actually illuminating, its the feature visualization of CNN and neural style transfer that help you make sense of what every part of a huge CNN can possibly do and what the hidden layers mean.

卷積神經網路

機構：deeplearning.ai
鏈接：卷積神經網路
時間：2018年1月12日
成績：100/100
主題：深度學習、鍊金術
點評：吳恩達老師的深度學習系列課，付費。算力逐漸爆炸，模型逐漸黑化，世界逐漸崩塌。什麼鬼...已經不想水了，然而掏了錢，不水白不水。

Python Data Visualization

Organization: Rice University
URL: Python Data Visualization | Coursera
Time: January 23, 2018
Grade: 97.9/100
Topic: python, data visualization
Review: Finished within free trial.

Python數據可視化

機構：米飯大學
鏈接：Python數據可視化
時間：2018年1月23日
成績：97.9/100
主題：Python編程、數據可視化。
點評：我只是想試試能不能在試用期免費刷一門課，然後就很雞賊地刷掉了。可以，這很環保。

Sequence Models

Organization: deeplearning.ai
URL: Sequence Models | Coursera
Time: February 23, 2018
Grade: 100/100
Topic: deep learning, natural language processing, black magic
Review: This is the last course of the deep learning specialization by Professor Andrew Ng. Its said this one has been postponed for twice already, even this session was three days late for its declared launch date. I can possibly imagine what kind of tight schedule theyve been working on to put things together. The course itself is good, but too sloppy. The learning experience is not quite enjoyable for a paid course. I expected better.

序列模型

機構：deeplearning.ai
鏈接：序列模型
時間：2018年2月23日
成績：100/100
主題：深度學習、自然語言處理、黑魔法
點評：吳恩達老師的深度學習系列課，付費。這是最後一門了，顯然連他們自己都在趕工，連續推遲了好幾次，在最後一次還延遲了兩天才開課。我現在搞的工作是NLP相關，正需要這方面的知識。但我去面試之前，這門課居然還沒開課。我就這麼零基礎（當然是NLP零基礎，不是CS零基礎）去面試了，也是坑了個爹。課程中出現了一些錯誤和瑕疵，估計他們實在太趕了。總體來說，這門課的品質對吳恩達老師的聲譽恐怕是有點損傷的，畢竟他一向以嚴以律己寬以待人的形象而受學生們歡迎，這個付費品質讓人不太滿意。

Bitcoin and Cryptocurrency Technologies

Organization: Princeton University
URL: Bitcoin and Cryptocurrency Technologies | Coursera
Time: March 24, 2018
Grade: 92.3/100
Topic: bitcoin, blockchain, distributed computing
Review: Im glad Princeton presented a course for cryptocurrency for tech professionals. Id really love to learn some stuff that have great potential for a long-lasting impact in industry, not a tulip bubble or some foolish zero-sum games. Thats why I choose to view blockchain and cryptocurrency as two separate ideas, of which the former is of more value to me.

比特幣與虛擬貨幣技術

機構：普林斯頓大學
鏈接：比特幣與虛擬貨幣技術
時間：2018年3月24日
成績：92.3/100
主題：比特幣、區塊鏈、分散式計算
點評：普林斯頓大學提供的區塊鏈技術課，拋開騙子、韭菜之類的破玩意兒，從技術上講解了區塊鏈、虛擬貨幣、智能合約等技術，也從密碼學、信息安全、工程倫理等角度給了一定的思維拓展。編程作業難度不大，但想得滿分很難。王子屯的公開課總是給人一種春風拂面的感覺，乾淨又清爽。之後有時間的話，我打算把密碼學1和密碼學2刷掉。我專門記了一篇筆記：Learning Notes for "Bitcoin and Cryptocurrency Technologies"。

Financial Accounting: Foundations

Organization: University of Illinois Urbana-Champaign
URL: Financial Accounting: Foundations | Coursera
Time: April 7, 2018
Grade: 100/100
Topic: finance, accounting
Review: This is the first course of the Financial Management Specialization, I take this course to learn something about accounting, as a prior knowledge to financial engineering. The peer-reviwed assignment is good, though not enough people are willing to pay to join up, so you dont have as many classmates around the world to share insights with. **Still, peer review is a very idea-inspiring process,** its quite different from working on computer programms and expect things to work exactly as you command. You actually seek difference from your own. **Investopedia is a good place to drop by.** You never get disappointed.

金融會計：基礎

機構：伊利諾伊大學香檳分校
鏈接：金融會計：基礎
時間：2018年4月7日
成績：100/100
主題：金融、會計
點評：這是金融管理系列課程的第一門，會計基礎課。我打算把這系列修完，作為金融工程的預備課程。Investopedia是個好地方，學習一個。

Financial Accounting: Advanced Topics

Organization: University of Illinois Urbana-Champaign
URL: Financial Accounting: Advanced Topics | Coursera
Time: April 9, 2018
Grade: 99/100
Topic: finance, accounting
Review: This is the second course of the Financial Management Specialization, still a short four-module course, with 4 quizzes and 1 peer-reviewed assignment. The number of participants seemed a bit low, I had no choice but to wait a whole day before getting any response and having my assignment graded. Still, Im much luckier than the fellows I helped review. They actually waited a week or a month, you believe that? My god. Im glad I helped them out.

金融會計：進階

機構：伊利諾伊大學香檳分校
鏈接：金融會計：進階
時間：2018年4月9日
成績：99/100
主題：金融、會計
點評：這是金融管理系列課程的第二門，其實一點都不進階，把一二兩門課合稱為會計學入門還差不多。好像也沒入門啊......水啊水。其實這兩門課就是教你看財報。

之後每完成一門課，我都會在此更新的。

// Whenever I finish a course, Ill update it here.