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

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

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

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

*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

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

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

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

Thank You MIT

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

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

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

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

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

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

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

Anyone here because of the damn quarantine?

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 : [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)

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

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

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

This Course is gold. This quality does not exist anywhere else. I read the book, watched all the videos, solve the priogramming assignments. Thanks MIT and Professor Guttag! You can find assignment solutions for 6.0001 and 6.0002 on my github account: github.com/emtsdmr

What a personable prof!

I just have two words: Thank You

A good example of the global vs local optimum is: Problem: consider vals = {1/2, 1,3, 1,4}, and then find the subset of values in vals such that the sum of values in this subset is as large as possible, but is also constrained to be 5/8. However, not confined to taking this greedy algorithm, you can see that 1/3 + 1/4 = 7/12, which is less than 5/8, but better than our greedy alg result of 1/2. So therefore the point is that greedy algorithms give you different results to the knapsack problem depending on what your metric is (our greedy metric here was 'next largest', but we could have chosen something else. In fact, 'next smallest', would have gotten us the global optimum solution!). "local optimum" in this context refers to the optimal solution *for a given metric* ('next largest' - which yielded our result of 1/2), which as mentioned, isn't necessarily the same as the best possible global solution (our result of 7/12) to a knapsack (optimisation) problem.

What a cliffhanger to end on! :)

I Love the way they teach us .....Awesome I have great experience .....#Great Content and Also Valuable ......

The 'no good solution' statement for 0/1 knapsack problem is true if we assume P not = NP

this food rewards reminds me my relationship with my dog. :) Anyhow, good explanation and overall definition of such concepts!

Wish I could attend in person. Great lecture, just sad not enough interaction.

thanks , mit

he is legend ,great explainer

This John Guttag guy, I like his style

Thank you I enjoyed it

very good lecture

Thank You

Fantastic course, thank you to MIT, like many here I will donate when I start earning!

thank uu mit ocw

Thank you MIT

4:10 Start

This is amazing

It is such a shame that this video has 287K views and the last video has only 20K views, why do people don't complete the course?

How optimization works? 6:08 I. E. Route by car from a to b Objective to min travel time So objective function = sum( mins spent) from a to b On top of that layer a set of constraints(default empty) Fast way Boston by plane but impossible on a 100 budget Timw: to be before 5 pm While bus only 15 but impossible before 5, infer better to drive Constraints help elimination some solutions This asymmetry is handled differently Knapsack a burglar with limited space, items more than he takes 11:00 contonus problems solved by greedy algorithm takes best, nice on 0 1 knapsack: decision affects other decisions I could end up multiple solution 1300 or 1450, greedy does not guarantee best answer Assume n items: 0. Total max w 1. Set available l 2. V item is taken 16:30 bruteforce algorithm Generate all subsets (of items) From a powerset 23:31 Key function used to sort the items (based on. Some criteria Take item subtract calories Next time best time found out (but can't leave yet) 🤔 If an item makes it overbudget "wait and see" check others, then Algorithm efficiency? Python built in timsort Same as quicksort= same as mergesort n log n N (len items) N log b + n (constant) Order n (log n) Door for large number (1M) Not for cost but cheap ones first We get different answers with greedy Only local optimal solutions chosen each point Hey stuck local points boy the best one

Give this man a Nobel prize in teaching !!! ##

Great!

thanks

it feels funny to hear absolute silence in response to some questions, the way that even MIT students dont know or are afraid of answering wrong

I can't believe this is for free

Cheers!!!

I love this guy! Man literally threw out candy to encourage students to answer questions, that’s so cute lol

Easy introduction; Using human mind as an example for understanding of how mental congnition takes place in logic sets, to more logic sets, taken into relativity to personal information that is believed from the correlation of past believed information that foundationally supports anything believed by that individual to be true. *Because, beliefs equal what we deem to be real (more on that later). For example, Artificial Intelligence is computationally created (unintentionally), but found to be necessary based upon exposure to beliefs or purposely created by the creators (humans) without knowledge of the methods that are being used for an outsider source of creation. This is the greatest factor of creation. It is statistically possible to re-create what has been proven and even possible to prove that nothing is random in the event that it be understands the mirrored language in which it comparatively recognizes as belonging to a "concious" observation of some outcome. If the created language is is newly acquired and uknown, then no phenemonela is observed for validate its existence. Therefore, no new DATA is confirmed and a moment for observational phenomena was lost (some call this luck). In the event that new. Infornation is realized and then it turns into data due to concious observation then it will be consciously compared to what is known in some context that cognitively gives validation to a past experience that has been deemed factual and correct, therefore creating a sense of beliefs. *If the Universe offers assistance to the creation of other Universes and its nature is to produce systems that are in mirrored in reproduction then it would seem relative. Some of these observations would be similiar, metaphor like, opposite of, symbolically important or whatever is conciously observed and to be factual or possibly thought of and believed to somehow shaped or formed the connected understandings of the unique observer. We could jump into many acdemic subject matters and show how concious creation through cross sourcing one subject matter to the nex subject matter and to helps to identify the creation of anything, because everything is a "system" persay...

Woahh, nice video. Didnt expect to use knapsack algo in data science... We learnt it in design and analysis of algorithms.... Interesting idea.. i got a idea.. maybe i can do something innovative 🤔 By the way love from India

damn i'm hooked

thnx MIT

31:40 The moment the professor discovers that no one understood anything.

This Parachute is a knapsack! XD

the length of the list of names is 9, but the length of the list of values and calories is 8. Therefore, no value or calorie is asigned to the cake. But the lecture is really great...minor mistake...

Just finished the exam of this... What if this uploaded few months ago...

Timsort is a variant of Quick Sort? AND QS has worst case complexity similar to merge sort?? I guess I don't understand Computational Complexity that well :(

I code excactly like in the video but when i run it, the error name “Food” is not defined in line 17 (build menu) appear. Does anyone has any ideas ?😢

Thank you from Algeria

[36:00] I don't get why we get different answers in the greedy algorithms as long as we use the same items and the same key function It does local optimization, but it does not mean that local optimization is different each time we run the program given the same parameters

6:14 here should it not be objective value than a function? What am I missing? Minimum time would be a value right?

Are the numbers inside the 'values' array randomly picked by the instructor or the does it act as a grading scale for each menu item?

cool

LOLLLL I love when no one can answer his questions. Omg, I feel so bad for that professor.

Where did the I[i] come from? Shouldn't it be L[i]?

Thanks for the assist Ana (heart emoji)

Thank you for these lectures. If I come into money I will make a large donation.

What about a genetic tournament algorithm?

Is there a specific order in which I should watch the different playlists for ML?

👍Gud morning gud video

36:06 Do you mean calories as weight, sir?

Excelente, ¿podrían igualmente subir vídeos de física y matemáticas con subtítulos en español o traducidos al español? Gracias.

Boy, talk about your cliffhangers.

Professor knows to solve complex optimization problems but don't know what to do when the screen freezes. Calls the assistant.

Which book is used for this course and how I can exercise on the different topics concerned the course? If there are any...

28:42 what is "item" that used for ?

error?

What are the prerequesites of this course?

La Casa De Papel knows the 0/1 knapsack problems omg!

32:14 i feel so bad for the prof... he's trying so hard to build a connection with his students...

source code of that example program please.

Dr. Anna Bell from 6.0001 pops out in this video... Did any of you guys notice???

20:44

26:30

32:31 really no one can answer !!

27:11 so n = len (item) has a computation time of O (n log n) huh? I just understand now. thank you sir

why are there 9 names and only 8 values and claoires

36:48 donut should have 95 in calories instead of 195 showing in the result, and apple should be 150, not 95.

Quicksort worst case is O(n^2). The professor probably wanted to say average case complexity.

so i have learned machine learning ,python,sql,tableue,powerbi,flask in 10months thanks to corona ugggh

RE: Carnegie Hall Joke. --> Is that where Inglorious Bastards got the line from?

One more question: Why is density function returns self.getValue() / self.getCost()

What programming classes should I take before learning this course? Thanks

I could have got the candy reward it was so obvious that the answer is Food .

Great course...but objectively speaking...we are always looking for a=b...now subjectively speaking...a=whatever the *user* wants... which brings us back to why we stick to frigging applying a linear transform on everything...

hey help me. is he using phyton x,y?

Finally i can double speed the lecture.

11:44