算法与计算机科学jeffe.cs.illinois.edu编辑精选
算法（Jeff Erickson）
Algorithms, by Jeff Erickson
2018 — 2022，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
伊利诺伊大学 Jeff Erickson 教授的算法课教材，免费在线开放。多位读者称赞它是所见过的对最小生成树、FFT 等主题最清晰的讲解，行文幽默、注重直觉，是难得一见的“读得下去”的算法书。
为什么经久不衰：免费、写得极好的算法教材，多年反复登上 HN 首页，对自学者和在校学生都同样有价值。
HN 评论精选
亲历学生盛赞作者的教学水平，讲义质量极高
Jeff Erickson 是我 2012 年的算法课教授，他是我心目中理想的那种表达清晰、充满热情的教育者。这些讲义我考前反复读过很多遍。课上流传一个趣闻：任何考题只要写“我不知道”就能得 25% 的分数——这是对承认自己不足、并替助教省去 decipher 废话时间的奖励。Erickson 教授，如果您看到这条，感谢您成为我大学时代最好的老师，并把写得如此漂亮的讲义公开给所有人。
primitivesuave查看英文原文
Jeff Erickson was my algorithms professor in 2012. He exemplifies the articulate, passionate educator that I wish I had for my other CS subjects. I recognize many of these notes having read them many times in preparation for quite difficult exams - a fun anecdote shared among people who've taken the class is the 25% credit given on any exam question just for writing "I don't know", effectively a reward for acknowledging your own shortcoming and for saving the TA the time to decipher a bullshit answer. Professor Erickson, if you're reading this, thank you for being the best educator of my college days and for making your beautifully-written notes available to everyone.
即使对熟悉该领域的读者，这本书也幽默清晰、令人爱不释手
我大概率不是目标读者（早已熟悉这些内容），但这本教材读起来是一种享受。才看到第 14 页，我已经数不清笑出声或为某个观点叫好的次数了。大多数算法书要么枯燥，要么纠结于形式化细节，要么（更糟）在算法里隐含地加入优化却不解释哪些是正确性所需、哪些是效率所需。但这本书里扑面而来的个性极有感染力，真的让人欲罢不能。
Twisol查看英文原文
I am, in all likelihood, not in the target audience (having already spent much time with the material being presented), but this textbook has been a joy to read so far. I am only up to page 14, and I already need two hands (in base 1...) to count the number of times I've laughed aloud or cheered a particular point being raised. Most algorithms books are dry, or they're obsessed by particular formal details, or (worse) they implicitly include optimizations in the algorithm without explaining what is needed for correctness and what is needed for efficiency. Having tutored on one of the books mentioned in the prologue, it can be a real struggle to gain a true intuition for algorithms when you can't yet tell the difference. But the sheer personality contained within this book is infectious, and it really is something of a page-turner. (Lest you think I haven't gotten to the stuff that "matters", I have read through the chapter on depth-first search -- again, an enjoyable read!) What a great book. I'm definitely recommending this to my friends.
最小生成树和 FFT 章节是同类讲解中最清晰的
说真的，我非常喜欢这本书里读过的部分。尤其是“最小生成树”和“快速傅里叶变换”两章，是我见过的对这两个主题最清晰的讲解。
chillee查看英文原文
FWIW, I really liked the sections of this book that I've read. In particular, I read the sections on "Minimum Spanning Trees" and "Fast Fourier Transform" and found them the clearest treatment of the subject that I've seen.
作者解释不提供习题解答的原因：为了学生真正练习并获得诚实反馈
作者在网站上解释：请不要向我要习题解答，即使是教师我也会拒绝。我承认这限制了这些材料对自学者的用处，但我在优化本校学生的学习体验——作业的意义不是解出那道题，而是练习一类问题并获得诚实的反馈。有解答可查时，学生更容易依赖解答而不是自己思考，练习更少、反馈更不真实，考试和课程表现都更差。
asicsp查看英文原文
From https://jeffe.cs.illinois.edu/teaching/algorithms/hwex.html#solutions >Please do not ask me for solutions. With very rare exceptions, I will say no, even if you are an instructor. I recognize that my stance limits the utility of these materials, especially for self-learners, but I'm trying to optimize the learning experience of my own students at Illinois. The point of homework is not to solve that particular homework problem, but to practice solving a type of problem and get honest feedback on your progress. I've found that when solutions are available, my own students are much more likely to rely on them, rather than trying to figure out the problems themselves, which means they get both less practice and less honest feedback, which means they do worse on exams and in the course overall. Interesting. I was asked multiple times for solutions to my self-pub ebooks that I relented. I didn't add them initially as I wanted readers to solve by themselves or ask for help if needed (and I did get a few mails, saw one of them asked on stackoverflow as well). See also https://github.com/tayllan/awesome-algorithms for more learning resources, practice problems, visualizations, etc.
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