Professor Yu is in his mid-twenties and teaches one of the most subscribed courses at Harvard. Amazing!

Listing some subchapters for future reference 01:07:41 - A* Algorithm - "heuristc search" for a singleagent 01:14:27 Adversarial Search (Tic-Tac-Toe) 01:39:07 Optimization - Alpha-Beta Prunning 01:46:53 Chess -Depth Limited Minimax 02:14:05 Inference Algoritms -Model Checking 02:32:42 Knowledge Ingeneering - Clue 02:43:04 Logic Puzzles - Harry Potter 02:56:16 Inference Rules 03:22:26 Inference by Resolution 03:39:39 Uncertainty - Probability theory 03:49:16 Conditional Probability 04:05:58 Bayes Rule 04:13:40 Joint Probability

Stop reading comments , and follow the lesson

What I love about these Harvard CS50 videos is the speed they talk and explain things. It's captivating.

Grateful to the Harvard University for providing this course. Thank you brian yu and all of them who are behind to provide this course.

Brian is incredibly organized and polished. If I had professors this good back when I was in school for CS it would have been a vastly more productive experience.

dude i cannot thank you enough, what a time to be alive :)

This is amazing! Both the course and the teacher. Thank you very much for sharing this.

It's the best thing I have ever seen on KZfaq. Great job, thank you for every minute of this course.

Thanks for putting together this course in one video.. Thank you so much for all of the free courses you upload.

Thank you so much! The only platform who made me take interest in programming after spending 3 years in my 4 year Bachelors degree of CS. CAN'T THANK YOU ENOUGH!🙏🏼

When i did my Masters in mathematics, only one of the professors at my school had solid understanding of these topics. I learned as much as i could, but he was overwhelmed with students. I am grateful for these videos!

There are many unique aspects of those videos, but what is really nice is the depth of explaining such concepts. Even in a university, they usually cannot go that deep mostly due to time constraints.

Almost two hours in and it's so good; I understand the concepts really well. Thank you, Mr. Brian. I am really enjoying the course!

Thank you so much for all of the free courses you upload

I can’t afford to miss this. This weekend I’ll go in depth this course. A value bomb! Thank you profesors!

I love the way he explains everything. Thanks for the course

Learned a lot from this video. Two thumbs up. For the specific example he gave, the number tile, I think "reverse engineering" approach, couples with the AI process he described, will solve the problem more efficiently. That means I start with the end sequence = numbers in ascending order left to right, top to bottom. Then I map out all possible paths to "chaos" state = all tile arrangements that are not the end sequence. I can determine all possible chaos states = 16! = 2.092279e+13 assuming the hole is also a tile. The possible paths should be much less than 16! because each move along the way to a most "severe" chaos state is a chaos state itself. The map will look like a family tree, starting with the end sequence, and the last progeny of each branch is the most "severe" chaos. When user enters a chaos state, the algo finds where it is on the family tree, follow the reverse path/moves back up to the end sequence. The reverse-engineering approach will only work well when the goal/end is well defined.

Thanks for putting together this course in one video.

Lupet nito! Ang linaw pa magsalita. Thank you!

Prof. Brian Yu, Just *amazing*!!! I am at the tail end of my career and viewing this just out of curiosity and cannot stop watching this video! You just have a talent for communicating these concepts! Just amazing, and a very big Thank You!!

Thanks for this wonderful content. Professor Brian has done an incredibly well done job. I learned much and Thank all the persons involved in producing these lectures. Much grateful to Harvard University for sharing this. Professor Brian, you are a GREAT Communicator!

Amazing content, thank you CS50 Team. You are on top of the world

Had Brian as a TA a few years ago, he’s an amazing guy. Malan is also the best professor I’ve ever had. Amazing to get this stuff for free nowadays! No excuses for anyone

I am so glad I found this, am doing an AI Engineering Course with another institute, you've just made it so much easier to understand.

This is unbelievable. Much appreciated.🛐🛐🛐

Thank you so much. I don't recall ever enjoying a lecture series as much as this. Great work! I hope to see a follow up in a few years.

So happy to be able to revise this stuff from university thanks so much!

Que contéudo maravilhoso, era tudo que eu precisava, grata

Very useful and informative. The course provides in-depth knowledge. I learned a lot. I am grateful for providing this great course.

This is amazing! I'm a beginner with zero coding experience and I'm understanding this with clarity. Professor Yu is stellar!

- [00:00](kzfaq.info/get/bejne/a7SXgcyUprTdoYE.html) 🤖 The course explores foundational concepts and algorithms of modern artificial intelligence, covering topics like graph search algorithms, optimization, reinforcement learning, and more. - [03:16](kzfaq.info/get/bejne/a7SXgcyUprTdoYE.html) 🛣️ AI aims to solve problems by searching for solutions using various actions and transitions between states in a state space. - [07:00](kzfaq.info/get/bejne/a7SXgcyUprTdoYE.html) 🧩 States represent configurations, actions are choices, and transition models define the outcome of actions. Goal tests determine if a state is the goal, while path costs measure the cost of actions. - [11:31](kzfaq.info/get/bejne/a7SXgcyUprTdoYE.html) 🔄 A search problem involves exploring states using a frontier, a data structure containing states to be explored next. A loop-based search algorithm iteratively explores the frontier, considering possible solutions. - [19:35](kzfaq.info/get/bejne/a7SXgcyUprTdoYE.html) 🕵‍♂ The search algorithm involves removing nodes from the frontier, analyzing their state, parent, action, and path cost to navigate the search space and find solutions. - [33:09](kzfaq.info/get/bejne/a7SXgcyUprTdoYE.html) 🔄 Depth First Search (DFS): Explores one path until a dead end is reached, then backtracks and tries another path. Can lead to non-optimal solutions. - [36:24](kzfaq.info/get/bejne/a7SXgcyUprTdoYE.html) 🌐 Breadth First Search (BFS): Explores all possible paths at a given depth level before going deeper. Guarantees optimal solutions but may require more memory. - [38:51](kzfaq.info/get/bejne/a7SXgcyUprTdoYE.html) 💻 Code Implementation: The video demonstrates code implementation of DFS and BFS for solving mazes, highlighting their exploration strategies and memory usage.

Best course and so perfect explained, thanks Brian Yu

This was clear, concise and conclusive course, with a professor that not only knows the topic very well, but does have a way of helping us build the knowledge as a master! Thanks, Brian Yu!!!!

Professor Yu is amazing at imparting knowledge to beginners.

This is teaching at its finest. Thank you, Harvard, for your generosity. I am 49 and glad to be living in a time where such precious knowledge is provided for free. It's amazing how one can gain knowledge in abundance, free of charge.

After watching a couple of thousand presentations on youtube, this is hands down number one.. Number one in clarity, fluidity, timing, content and expression. Thank you Mr. Wu, hat is off.

Çok teşekkürler Sinan hocam. Emeklerinize sağlık.

Love the BFS concept

This is the best lecture trailer in computer science I have ever seen. Observe that he started naturally from states and agents(state machines) to gradually reach AI and ML.

I am very grateful for the excellent free courses at Harvard. ❤

thanks for disponibility this course, i really apreciate the initiative. kisses from brazil

This is a lot of information. I love it. Thanks for sharing

This is the video that actually gets to the nitty gritty details as to how an AI actually works rather than just explaining its concepts and its history or whatever, really a gem in a coal mine if you ask me .

Thank you for sharing this with the world.

Clear Concise and complete!

This is a must watch video for every computer science student!

I am surprised by the complexity of the study it requires, just to develop the AI that plays these games. Kudos to the professor who is just excellent at this concept in the simply digestible form.

harvard university is really changing the world by giving acceses to the inovative minds all over the world

Awesome content. Thank you Brain!!

A very much thank you sir for supporting me in this journey. I will look forward to learn more and more.. Thank you

The way Bryan explained a* search is so much better than the other videos i have watched!

Prof. Brian Yu, he's got a knack for making even the most complex topics seem like a walk in the park.

This is a real piece of art that I have witnessed. Kudos to the professor always impress me with the depth and clarity of his knowledge.

This is a great course for me to though ML after my 12week online course from MIT!

Excelente explicação!

A very informative course, i learnt a lot of new techniques and information thank you.

Terrific course. Watched this as part of my sabbatical. I'm an experimentalist (genomics/genetics) and this helps bridge the gap with computational approaches to make sense of large data sets and make functional inferences.

Lectures with clear pronunciation are very easy to listen to and understand

- This course from Harvard University explores the concepts and algorithms at the foundation of modern artificial intelligence. - The course covers topics such as Graph Search algorithms, classification, optimization, reinforcement learning, and machine learning. - The course is taught by Brian Yu as part of the CS50 program. - The course starts with an introduction to how AI can search for solutions to problems, such as playing a game or finding directions. - The course explores how AI can represent and use information, including uncertain information. - The course covers optimization problems and how AI can learn from data and experiences. - The course includes an exploration of neural networks, a popular tool in modern machine learning. - The course also covers natural language processing, where AI learns to understand and interpret human language.

Thank You very much Professor.

I really thank you guys for the content hope all the best to you and the team, people who put so much effort into these course.

notes: frontier, action function, transition function, depth-first-search, breadht-first-search, greedy-best-first-search (with heuristic closest to goal function), manhattan-distance, A*search=>optimal solution, admissible heuristic never overestimates the true cost, classical search vs adversarial search, minimax

thanks a tonne for this 🙂

Section “knowledge”, “uncertainty”, and “optimization” are so interesting. Even though you don’t directly use them much in an industrial setting, they’re still beneficial to problem solving. Nevertheless, they’re always skipped in most MOOCs.

Wonderful production!!

I have an exam tomorrow in chemistry, and I can't seem to stop watching this lecture today! Man, I'm gonna be screwed!

Brian Yu is best, i wish i had half of his talents.

11:44 It is like useReducer in ReactJS, you pas state and and dispatch action

Absolutely amazing!!!

Wow amazing. Thank you so much

This brought good memories of a course I took in 1989 while doing my computer science degree. We did the same algorithm and many similar ones. For the first one, the maze, we used a recursive algorithm or loops, traversing graphs and trees. I did not know we were doing AI at that time :) In fact, this is not AI, but without the AI attached to the name, people may not watch the video.😁 This video is fantastic as it illustrates the power of optimizing algorithms to improve performance. The time complexity of such an algorithm as the maze is exponential.

Thank you! At age 62, loving every video you release!

Learnt a lot will go for Deep learning from here along with web development with python as it will help to deploy model.... good work!

Nice job Steve 🔥

This man is the Hero

fantastic learning resource. please can you share the github link for the sourcecode from the demoes in the lectures. I couldnt locate it from the cs50 repoes on github

This is amazing! Thank you

I love your channel and I wish your courses help me up in developing my career, thanks a lot

Can someone create a time stamp specifying which minutes are certain algorithms and Data structures in the video?

I find it fascinating how people are shocked when I tell them that I majored in philosophy and work in AI. Usually, they respond with, "Wow, completely unrelated fields." Little do they know that propositional logic is at the core of both philosophy and AI.

I cant believe it! you are the best !!!

9:14:08 Isn't it better to work with bias of -0.5 in case of the OR unction 9:16:51 and bias of -1.5 in case of the AND unction

thank you very much for this wonderful content ! is the source code actually available ?

I am planning to complete this course within a week, I'll update each time I complete for each day with the time stamp: Day 1: I just finished Search functions fully 1:51:56 Day 2: knowledge completed (I forgot to edit the comment because I actually went to Harvard website and registered for the course and studied there) Day 3: half way through probability Day 4: I'm done with probability and now i'll continue back in few days Day n: Idk what day is this but It's been more than a month I think so, but finally completed the course and now I need to start making projects.

thanks for this✨

Hello, thank you for posting this course. I did the CS50, and this course also interested me, but it seems to date from 2020. Is it up to date now? The description says "course has been updated for 2023" but the course looks identical to the one on the edx site, so I wonder what's new there.

Can't wait to get hand on this before my next semester ❣️

Amazing content! Website also!

This course is amazing. It was my first encounter with an AI lecture, and I enjoyed it. I really enjoyed the Professor's teaching method, when he introduces new concepts, he starts with the easy to understand components and increases the complexities of the contents gradually. Each segment is accompanied with numerous real-life examples that make understanding the concept easier. Keep up the good work.

I'm 104 years old and I'm loving this. Thank you 🙏🏼

As a student in Aisa, I sincerely appreciate that one of the world's best universities can provide its wonderful courses for free. 回覆

Does anyone have the complete list of visualization types (e.g. Constraint graphs) for this course? It would help a lot, thanks!

Thank you, Mr. Brian.

39:00 code to solve maze problem

Any prerequisites for this course especially should we know python before taking this course?

Brian yu's speech is really perspicuous and crisp pronunciation

Great course, just deep enough so to be not superficial. 😊

The information we got for free... there should be no excuse.

By any chance would you also put engineering courses like mechanical engineering on this channel?