Courses I have completed are :
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Topics in Deep Learning (10-707)

I took this course in Fall 2017. This course covered some of the theory and methodology of deep learning such as Monte Carlo Methods, Deep Generative Models, Generative Adversarial Networks (GANs) etc.

Fall 2017
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Machine Learning for Large Data Sets (10-605)

I took this course in Fall 2017. This course will provide a thorough practical understanding of Machine Learning.

Issues discussed in the course : scalable learning techniques, such as streaming machine learning techniques; parallel infrastructures such as map-reduce; practical techniques for reducing the memory requirements for learning methods, such as feature hashing and Bloom filters; and techniques for analysis of programs in terms of memory, disk usage, and (for parallel methods) communication complexity.

Fall 2017
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Speech Processing

I audited this course in Fall 2017. This course provided a thorough practical understanding of Speech Processing techniques.

Issues discussed in the course : Automatic Speech Recognition, Text to Speech conversion, Speech to Speech Translation, etc.

Fall 2017
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Java for Application Programmers (08-671)

I wanted to strengthen myself in Java concepts and prepare myself for a better career in the industry.

The course covered all fundamental topics required for programmers such as concurrency, simple data structures, network, interfaces etc.

Fall 2017
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Negotiation (94-800)

I took this course in Fall 2017 as a fun supplement to my loaded course work and job search. The structure of the course was totally different to any other course I had taken. I was very happy to interact with students from different backgrounds, experiences and educational streams.

Fall 2017
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Artificial Intelligence - AI (15-780)

This is a graduate course which teaches various algorithms and techniques in AI like search algorithms, intelligent agents, AI planning, reinforcement learning, graphical models, machine learning, multi robot systems, convex optimization and more.

Spring 2017
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Computer Vision (16-720)

I chose Computer Vision because I like the idea of being able to observe results on images and videos. It was the logical step after Machine Learning.

Topics covered include image formation and representation, camera geometry, and calibration, computational imaging, multi-view geometry, stereo, 3D reconstruction from images, motion analysis, physics-based vision, image segmentation and object recognition.

I also became proficient in MATLAB because of this course.

Spring 2017
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Advance Algorithms and Data Structures (15-650)

One of the hardest classes I have taken.

Topics include: Run time analysis, divide-and-conquer algorithms, dynamic programming algorithms, network flow algorithms, linear and integer programming, large-scale search algorithms and heuristics, efficient data storage and query, and NP-completeness.

Spring 2017
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Natural Language Processing (11-611)

This class covered an entire breadth of topics in the Natural Language processing field. Topics included: Probability estimation, hierarchy of english language, sentiment analysis, machine translation etc.

We also got to pursue a project on Question-Answering system based on wikipedia articles. Check the projects page to know more.

Spring 2017
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Practical Data Science (15-688)

I had decided to study Machine Learning and Data Science long before I came to CMU. This is the first course I took at CMU.

This course taught me the fundamentals like decision trees, neural networks, active learning, estimation, bias-variance tradeoff, hypothesis testing, Bayesian learning, Naive Bayes along with data science techniques including map-reduce, decision trees, recommender systems etc.

There is nothing more exciting than computer programs that automatically improve their performance through experience and I was hooked.

Fall 2016
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Pattern Recognition- Theory (18-794)

I took up Pattern Recognition because I wanted to understand the basics of Machine Learning. It was a great platform for my future courses and projects.

The course covers decision theory, parameter estimation, density estimation, non-parametric techniques, supervised learning, linear discriminant functions, clustering, support vector machines, feature recognition etc.

Fall 2016
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Introduction to Computer Systems (18-600)

This course provides a programmer's view of how computer systems execute programs, store information, and communicate. It also serves as a foundation for courses on compilers, networks, operating systems, and computer architecture.

Topics covered include: machine-level code and its generation by optimizing compilers, performance evaluation and optimization, computer arithmetic, memory organization and management, networking technology and protocols and supporting concurrent computation.

It enabled me to become a more effective programmer especially in dealing with issues of performance, portability and robustness.

Fall 2016