So I want to thank you, for taking the algorithmic think thinking course with us, you know Joe, Scott and I are all very happy to have had you in the course, and we hope that you learned interesting stuff from the course, we truly believe in the significance and the importance of the material we delivered in this course. As we had mentioned, we had offered this course at Rice University for four years now, and it has been very successful. We offered you a version of that course here and we hope that you also got the same type of material and the same type of experience, that we also offered to Rice students. And I want to summarize briefly what we have, what is this process of algorithmic thinking, that we have been following. And what is it that we have done throughout this course. And as a reminder, you know, I defined algorithmic thinking from the beginning of the course, as a five step process. And that five step process, is really motivated by the way I look at research and the, the, my research involving, domains outside computer science. In particular biology. So the first step in the problems when I'm dealing with a biologist or when I'm working on a problem from biology. Is to understand the problem. What is it that, what is that the biologists wants from me? What is that input, that they're going to provide me. Are they going to give me DNA sequences? Are they going to give me homgeno sequences? Are they going to give me some information about gene expression? Are they going to give me a interaction network? So what is it that they are going to give me, and what is it that they want from me? One, the first, when I understand all these details, what is it that they want to give, what is it that they want to, to get back? Now I formulate the problem, and this is where math, comes into the picture. The formulation, the problem formulation, which is the second step in algorithmic thinking, is really mathematics, more than algorithms. Here we have to use our skills, in mathematics to represent the problem cleanly. For example, if the biologist has given me DNA sequences, I have to say, that these sequences can be best represented as strings, right, which are basically mathematical structures, that we have talked about, okay? We will present these as strings. I have to define some sort of a mathematical criterion that says, what are the, the constraints on these strings to make them DNA strings, what are the constraints on the output, in terms of what the biologist wants, as a feasible solution, and what is an optimal criterion. If a biologist wants a good, solution, what makes a solution good, right? We have to define that mathematically, so that an algorithm can run, on the on a space of possible candidates and find the good solution okay? So first step understand the problem. Second is formulated mathematically in terms of input and output. The third thing, we come up with an algorithm, okay? Once we come up with algorithm, we have seen some algorithmic strategies in the course. Including divide and conquer. Including dynamic programming, we have seen graph exploration and so on. Of course there's brute force, but we have seen that brute force is not, the most interesting or the most feasible thing in most scenarios. So the third step, we are interested in coming up with an algorithm. We can, the first thing that we think about, is, okay what algorithmic strategies are out there. Are, is the problem we are dealing win, I mean, with amenable to some of these strategies, or do we need to come up with a new algorithm or a new strategy to solve the problem. And this usually one of the most challenging steps in algorithmic thinking, because coming up with an algorithm for a problem from scratch, is not a trivial task. Okay? So, we have see, some of algorithmic techniques. But beyond this course, there are many more advanced courses. Whether they're like traditionally combinatorial, algorithms, or more modern algorithms that deal with statistical inference problems, and so. There are lots of courses out there, that we highly recommend that you take and explore algorithms, beyond what you have seen in algorithmic thinking. Once we come up with an algorithm. We make sure it is correct, we analyze its correctness, we analyze its efficiency. We have talked about, running time and asymptotics to make sure that we can, reason about how efficient an algorithm is before we even move to the implementation fees, because as we have discussed in this course, if the running time of an algorithm grows. A symptotic like two to the N, where N is the input size, and you know that the size of the input is going to be on the order of thousands or tens of thousands, you know that that algorithm is not going to be worth, implementing because it's going to, be unfeasible in practice. So, once we have come up with an algorithm, reasoned about it's efficiency and correctness, the fourth step is implementing the algorithm. You have done implementation throughout the, the course with us. Where every algorithm was implemented in a, in a programming language. You had to test it and you have to, to go back and forth between the pseudo code, and the actual implementation. As I said from the beginning. One of the interesting things of going from the pseudocode, to the, to the actual code, to the actual implementation, is that that step, presents us with opportunities, but also presents some dangers there. The opportunities, in, in the, in the form that you can actually use your implementation or coding skills to take pseudocode and make, actually, a more efficient implementation of it. Because when we present through the code it's basically we are describing what the algorithm does. Not how it does every step in detail. When we want to present how things are done in detail that's when we go to the actual implementation. And you have seen I hope, throughout the course, is that when we present pseudo code in the homework or wherever. And you are writing the implementation of that pseudo code. In many, many situations, you even made the, the pseudo code more, more efficient. Okay? But the second thing also as I said, there is the danger of going from a correct pseudo code, to an incorrect implementation. And again, you have noticed that in this course that sometimes when you implemented the, the piece of code. Even though the pseudo code is correct, you notice that you had to go through many iterations, of that implementation phase until you got it right, okay? So, the first, the fourth step is, is that implementation of the pseudo code, but it's not a trivial step by any means right? Because, you have to make the implementation efficient. You have to make sure it is correct. And there are, there is a lot of room for efficiency, and there's a lot of room for in-correctness, as well that you have to avoid. After we are done with that, the fifth step in our algorithmic thinking is to take that code, run it on the data and reason about it. And I would say, this is really what distinguishes our course, from a traditional algorithms course, where we had that application part. Where we try to emphasize this notion that, an algorithm is not just a mathematical object that we implement, for fun. Some people do have fun, with coming up with algorithms or implementing them. But usually they are part of a larger application that we are trying to solve or to develop. And the application component in, in the algorithmic thinking. Was designed, to illustrate this notion. So, we wanted you to take that implementation, apply it to actual data set, get an output, and reason about it. And every now and then even make connection to the symptotic analysis and so on that you have done in the theoretical part. Okay? So, again, this is the five step process of algorithmic thinking. Where, we basically understand the problem, formulate the problem, come up with an algorithm, implement the algorithm, and then analyze the, the original data that we were presented with, so that we give solutions. Okay. Algorithmic thinking is a very powerful technique, especially these days when, when computer science is spreading, in terms of applications way beyond traditional computer science. So now we, the community talks about X plus CS, or X plus computer science where X can be any domain. Biology plus computer science, physics plus computer science, social sciences plus computer science. So, algorithmic thinking, is actually everywhere in this fields. Okay? And we highly recommend that, this is just the beginning, that you start if you enjoy it or you like algorithmic thinking, that you start, honing more skills, that will allow you to practice this kind of pro process in practice, which basically means, you need to take more mathematic courses, more algorithm courses and so on.
