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theories and applications. We will also learn a variety of machine learning and deep learning <br />frameworks with PyTorch. The introduction to basic neural networks, convolutional neural <br />networks and recurrent neural networks is combined with the development of real applications in <br />the computer vision field, <br />MB3614 'taeticall,D;igitaLMarketing (1.5 unit§): <br />This course offers an in-depth understanding and hands-on experience in current digital <br />marketing, We will look at how to build and manage campaigns from small to global, including <br />multiple languages in multiple countries. Overview of the types of campaigns along with a <br />detailed look at tools and methods. This includes keyword research, SEO (search engine <br />optimization), digital advertising on Google and Microsoft Bing search engines, social media <br />marketing with Facebook, Instagram, Twitter, LinkedIn, email marketing, content marketing, and <br />web analytics with Google Analytics. This class enables students to design and carry out a digital <br />marketing campaign for any project. Each lecture is followed by practical hands-on work. You <br />will see how to use a tool and then you will do the work in class. There will be lots of examples <br />of digital marketing success (and failures) at companies. Discussion and your questions about <br />your projects or companies are strongly encouraged. <br />B626 Dalization for Machine Learnrng (1:5 units) <br />This course covers Data visualization and communication using Machine Learning and its core <br />models and algorithms for students in the Data Science Program. It covers all significant topics, <br />including graphics, discrete and continuous variables, clustering and classification. The objective <br />of the course is to provide students an overview of machine learning techniques to visualize and <br />explore, analyze, and leverage data. The course covers the use of data analysis and machine <br />learning to aid the development of visualization. Implement prototypes that use visualization to <br />explain machine learning models supervised, unsupervised, and reinforcement learning. Students <br />will be familiarized with broad machine learning and statistical pattern recognition topics, <br />including neural network training, classification, regression, and support vector machines. This <br />course will use different languages' frameworks to demonstrate machine learning techniques. <br />Students will use R and Tableau to complete the homework, assignments and projects through <br />the course. <br />B09 Mni$fNL, units) <br />This course covers Big Data and NLP on Cloud. It provides an overview of Microsoft Azure <br />Cloud Platform and a deeper dive of the data processing and NLP capabilities. Through a <br />combination of presentations, demos, and hand -on labs, students will learn how to design data <br />processing systems, orchestrate end -to -end data pipelines, build scalable, accurate, and <br />production -ready natural language models using cloud technologies. The latest NPL models, <br />including GPT3, BURT, etc., will be covered in this course. <br />Page 50 of 65 <br />