1. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science)

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
Instructor: John Guttag

Prof. Guttag provides an overview of the course and discusses how we use computational models to understand the world in which we live, in particular he discusses the knapsack problem and greedy algoriths.

License: Creative Commons BY-NC-SA

[smartdatalearning]

Professor Guttag gives simple and well understandable explanations for otherwise actually pretty complex optimization problems (especially digital optimization). It is so nice that MIT is making these lectures public

[antikoerper]

One of the best things about the age we live in is that we all have FREE access to amazing lectures like these from MIT, no matter where we are

[aerafine]

I am amazed that these courses are freely available. Thank you, MIT!

[leixun]

*My takeaways:*
1. Prerequisites for MIT 6.0002 2:16
2. What is a computation model 4:17
3. Optimization models 5:47
- Knapsack problem 8:04
- Solutions of knapsack problem: brute force algorithm 16:18, greedy algorithm 19:38 and problem with greedy algorithm 37:05

[AceOnBase]

I'm working on an MS in data science, and man do I wish I had this guy. My professors over complicate everything.

[marco.nascimento]

Great lecture. Really looking forward to dive into this second part of the course, thank you MIT for uploading those <3

[metaloper]

For anyone interested, this course starts in march 2021 in EDx. It's free with an optional certificate for $75.

[notagain]

Imagination expansion is the single most valuable skill to learn that can assist further learning in the future . This imagination comes in forms like mind palace aka the Art of memory, maybe (Learn how to Learn ) ... This lecture made me think about why i became interested in Machine learning and made the path seem less intimidating, which makes me glad that i found this lecture playlist and youtube channel

[raticante]

thank you so much mit, I am a colombian student and without you I wouldn't be able to take this kind of courses

[bengbeng]

this is the best teacher, i realized that most of mit teacher are great wish i could study there

[samtj]

Personal Notes.
1. Keyfunction serves to map elements (items) into numbers. Tells us what we mean by best. In this case, the professor wishes to use the one algorithm independently of his definition of best.

2. Lambda function creates anonymous functions (a great one for one-liners) by taking an input of parameters and then executes the ONE expression. (lambda <x1, x2, ..., xn>: [expression])

3. Greedy algorithms can't really bring you an optimal solution. Different approaches to greedy tests: greedy by profit/value (selects the biggest value first), greedy by cost (selects the ones with minimal cost in hopes of obtaining as much items as possible), and finally greedy by density (selects the one with the biggest value per cost)

[carlosfonseca]

Great content, teacher and course. Thank you so much for uploading this course.

[oluwadaraadepoju]

Hyperparameters tuning is making so much sense now!. Thank you so much for this.

[kinda]

İt is so nice that MIT is making these lectures public 🎉

[anarelle]

What a brilliant lecture and a amazing professor. He reminded me of what a pleasure it is to attend university.

[sandip.bantawa]

If you are confused when Wednesday is, yes it is 2. Optimization Problems on autoplay

[studywithjosh]

Just finished 6.0001. If you want to go through 6.0002 with me im starting today!

[domsjuk]

The fact that his name basically 'means' "goodday" in German and "abdominal label" in English cheers me up for some reason.

[naruto-]

Thank You MIT <3 will donate when i earn some money

[mrocpnable]

Great content and teacher.
A little remark in the code:
names values and calories are not of same length. names is 9 and cake is indeed excluded