Classes I Enjoyed

ECE381K - Machine Learning on Real World Networks

•Marculescu

Learned about network science and graph theory on real world networks such as social networks, transportation networks, and biological networks
Explored machine learning techniques on graphs such as graph embeddings and graph neural networks (Graph Convolutional Networks, Graph Attention Networks, etc.)
Built a graph neural network to predict contact tracing patterns on mobility dataset in Austin, TX

•GligoricSyllabus

Learned about various programming paradigms and different programming languages that implement these paradigms
Explored topics such as functional programming (OCaml), object oriented programming (Smalltalk, Go), and declarative programming (SQL, Cypher), and concurrent programming (CSP, Linda)
Learned about the frontend of a compiler such as lexical analysis, parsing, and semantic analysis and implemented a lexer, parser, and semantic analyzer for a toy language using Java and ANTLR
Built an application utilizing multiple programming paradigms to process and execute Cypher queries on a graph database, and visualize the graph using Smalltalk and OCaml

Learned about topics in digital signal processing such as sampling, filtering, modulation, and demodulation
Applied these topics on an ARM microcontroller by implementing a transmitter and receiver in C

Learned the fundamentals of natural language processing for text analysis, classification, and generation
Explored topics such as n-gram models, word embeddings, recurrent neural networks, and transformers
Built a Research Paper Summarizer by finetuning a pretrained T5 model on a dataset of research papers with associated plain language summaries

•Marculescu

Learned about performing machine learning and deep learning on edge devices such as microcontrollers and FPGAs
Explored topics such as quantization, pruning, efficient neural architectures, and Neural Architecture Search
Explored the interaction between model design and effects on hardware (temperature, power, etc.)

Learned the fundamentals or computer vision and 'teaching machines how to see'.
Explored topics such as convolution, edge detection, key-point detection/correspondence, camera models, and (deep) machine learning for computer vision
Built a basketball shot predictor using deep learning based pose estimation and gradient boosted trees.

•DimakisSyllabus

Learned the fundamentals of building machine learning and deep learning models using data to perform prediction, inference, and generation
Explored topics such as linear regression, logistic regression, neural networks, and convolutional neural networks, etc.
Participated in a class-wide Kaggle competition to perform binary classification on synthetic data and achieved 1st place in the class
Built a LoFi music generator using RNN's and VAE's as a final project

•YerraballiSyllabus

Learn the fundamentals of operating systems such as process management, memory management, and file systems
Explore topics such as concurrency, synchronization, and deadlock
Created a simple shell program in C from scratch as well as a toy operating system 'PintOS' in C

•ShakkottaiSyllabus

An introduction to the mathematical foundations of probability, statistics, and random processes
Explore foundational topics in probability such as conditioning, Bayes Rule, Random Variables, and Independence
Use foundational topics to expand into Bayesian and Classical inferencing techniques and Hypothesis testing
An introduction into random processes such as Discrete Time Markov Chains and Poisson processes.

•SamantSyllabus

Explore foundations of building a full-stack web app through NoSQL databases, APIs, backend development, and frontend development
Learn about software design patterns such as the singleton, factory, etc
Introduce topics in DevOps such as CI/CD principles as well as MLOps

•MokhtariSyllabus

Learned the mathematical foundations needed to perform linear signal analysis
Explored topics such as properties of linear systems, convolutions, fourier series/transforms (continuous and discrete), and Laplace transforms.
Explored applications of these tools such as filtering, and sending information over signals (AM Radio).

Learned the fundamentals of linear algebra using vectors and matrices
Explored solving linear systems of equations, vector spaces, eigenvalues, diagonalization etc
Applied concepts in linear algebra to explore concepts such as fourier series and discrete time dynamical systems

•SoloveichikSyllabus

Introduction to combinatorial algorithms, algorithm complexity analysis, and NP completeness
Expanded on the topic of proofs and proving the correctness and runtime of various algorithms
Cover classes of algorithms such as stable matching, graph algorithms, greedy, divide and conquer, dynamic programming
Implemented algorithms using structures such as arrays, heaps, and graphs

•NandekumarSyllabus

Expand upon software design topics in Java such as polymorphism, multithreading and event handling
Introduction to data structures such as trees, graphs, and heaps
Explore algorithms such as recursion, backtracking and traversal algorithms for graphs.

•GligoricSyllabus

Learn the basics of C and C++ programming languages
Introduction to data structures such as linked lists, binary search trees, and hashmaps
Explore topics in software design such as object oriented programming, time complexity analysis, and recursion
Implemented algorithms such as sorting and various binary search tree algorithms