Hi, welcome to Jay’s video. This video is going to be about my master’s degree and machine learning. I’m not done with a whole degree yet. I have a little bit less than a year left, but I’m done with all my mandatory courses and also almost all my electives. I got two electives laughs that I’m currently doing, and then my thesis, and then it was all done, so I thought this would be a good time to just do this video and let you guys know what? I’m studying the first course that I did was called. Artificial intelligence and it was an introductory course to different concepts and areas within a I suggest, for example, hidden Markov models planning search Eros takes decision Theory. Not so much machine learning. I think we only have like one or two lectures about neural networks at the same time. As the Ai course, there was also an introductory course to of machine learning, which covered all the basic concepts and models used in animals such as, for example, the difference between classification and regression supervised and unsupervised learning models such as decision, trees, support vector machines, Bayesian learning and nearest neighbors and many more after the introductory course in machine learning. I did an advanced course in the machine learning, which was probably the hardest course that I’ve ever done. It was about probabilistic methods used in machine learning and we just got to derive a lot of math formulas for everything. There was also a course in the first semester called an introduction to the Philosophy of Science and research methodology, which was about how to do research how to set up experiments and just build models. And that was a whole fall semester in spring. I did a course and artificial neural networks and deep architectures, and it was about different types of neural networks suggest, for example, Multi-layer perceptrons self-organizing maps, recurrent neural networks. Boltzmann machines, our encoders, deep belief, networks and how fuel networks and we got to implement all those different types of neural networks and also analyze them given different types of data. I also did the course in time series analysis, which was a math course with a lot of probability theory. We got to analyze different types of time series data and our and the focus was on AR and MA processes and not so much on SLS CMS or any other machinery models At the same time As a time series, I chose to do another course called speech and speaker recognition, which was about various methods used to analyze and process human speech. There was also a course called deep learning that. I was supposed to do in my master’s degree, but I did it already in my bachelor’s degree, but since it belongs to my master’s degree. I thought I could bring it up here as well. But it’s a chorus about state-of-the-art architectures used in deep learning. Today, the second year started with a course and data intensive computing, which is about how you handle massive amounts of data. Everything from how you store it in distributed file systems to no sequel databases such as Hbase or Cassandra. To how you process the data on multiple computers using frameworks such as MapReduce or spark. I also got to learn to program in Scala and that course I then decided to do a course in ethical hacking, which was a little bit random because it didn’t have anything to do with machine learning, but I’ve always been curious about how hacking is done, so I thought why not, and the course was very interesting. Because the only assignment that we got in the course was to freely hack into a network that was set up for us to hack, of course, and just in any way that we could come up with compromised the different computers by, for example, cracking passwords, sniffing traffic privilege, escalating and just finding Backdoors. And you got to like, use everything that you found on the Internet. There were no rules at all, just like free hacking after finishing those courses, I’m not doing a course in data mining, which is about retrieving information from Large-scale data. And I’ve only been doing this course for a week or so, so I don’t know so much about it yet. But so far, we’ve been covering how to find similar items and frequent itemsets from a large amount of data and I think in the future, the course will also cover recommender systems, which I’m really excited about learning. I’m also currently doing a course in natural language processing and we’ve only had two lectures in that course so far, so I can’t tell you so much about it yet, but I think it’s about various methods used to make computers understand human language in the form of text after this semester. I only have my thesis left, which I’m going to do in spring 2019 I don’t know yet which topic going to write about, so I’ll let you guys know when I find out. I hope you enjoyed this video too. And let me know if you have any questions about any of the courses that. I’ve done or my degree. You can always ask me them in the comments below and Ill. See you in my next video bye.