Courses I have completed are :
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Parallel Computer Architecture (15-418)

I will be taking this course in Fall 2016. Writing good parallel programs requires an understanding of key machine performance characteristics, this course will cover both parallel hardware and software design.

Topics include naming shared data, synchronizing threads, and the latency and bandwidth associated with communication.

Fall 2016
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Machine Learning for Large Data Sets (10-805)

I will be taking this course in Fall 2016. 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 2016
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Computer Graphics (15-462)

I will be taking this course in Fall 2016 because I want to branch out and learn new things tangential to Machine Learning. Also I love gaming. :)

This course provides a comprehensive introduction to computer graphics modeling, animation, and rendering. Topics covered include basic image processing, geometric transformations, geometric modeling of curves and surfaces, animation, 3-D viewing, visibility algorithms, shading, and ray tracing.

Fall 2016
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Machine Learning - PhD (10-701)

I took another Machine Learning course because I wanted to work on an open ended project.

This is a PhD course designed to give students a thorough understanding of the mathematics and algorithms needed for research and applications in machine learning.

Spring 2016
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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 2016
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Advance Algorithms and Data Structures (15-351)

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 2016
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Internet of Things (18-848)

This course was very different from the others. We talked about the upcoming and futuristic technologies in IoT.

IoT was a good literature survery of technology today and where it is going. We discussed the application of IoT in Sports, Cities/Transportation, Home, Retail, Healthcare and various IoT Platforms like Hardware, SoC, sensors, device drivers, IoT standards, Cloud computing for IoT and Bluetooth Low Energy beacons.

We also got to pursue an open ended project in the field without fear of faliure due to great support from our instructor. Also we ate pizza during our exam. :)

Spring 2016
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Machine Learning - Masters (10-601)

I had decided to study Machine Learning long before I came to CMU.

This course taught me the fundamentals like decision trees, neural networks, active learning, estimation, bias-variance tradeoff, hypothesis testing, Bayesian learning, Naive Bayes, Bayes Nets and Graphical Models, the EM algorithm, Hidden Markov Models, K-Nearest-Neighbors and nonparametric learning, reinforcement learning, bagging, boosting and discriminative training.

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

Fall 2015
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Fundamentals of Embedded Systems (18-342)

I took up Embedded Systems because I wanted to understand the basics so that I could explore "Internet of Things".

The course covers the integrated hardware and software aspects of embedded processor architectures, along with advanced topics such as real-time, resource/device and memory management.

Fall 2015
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Introduction to Computer Systems (15-213)

The course code is named after the zipcode at CMU. :)

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.

Spring 2015
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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.

This was the first course I took at Carnegie Mellon and it was one the best.

Spring 2015
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Systems Engineering (16-850)

Systems engineering examines methods of specifying, designing, analyzing and testing of complex robotic systems. The focus is on robotic system engineered to perform complex behavior.

I took this course because the prospect of designing and implementing a robotic system excited me. This course taught us the principles and processes of systems engineering and we applied it to the development of robotic devices.

Spring 2015