My Overall Experience

My Machine Learning Experience

mindmap
root{{My Machine Learning Experience}}
  (Deep Learning)
    Convolutional Neural Networks (CNNs)
    Recurrent Neural Networks (RNNs)
    Generative Adversarial Networks (GANs)
    Autoencoders
    Transformers
    Deep Reinforcement Learning
  (Supervised Learning)
    Classification
    Regression 
    Emsemble Methods based on problem type   
  (Unsupervised Learning)
    Clustering
    Principal Component Analysis (PCA)
    Independent Component Analysis (ICA)
    t SNE
    Self Organizing Maps (SOMs)
    Generative Models
  (Natural Language Processing)
    Text Classification
    Named Entity Recognition (NER)
    Sentiment Analysis
    Language Models
    Neural Machine Translation
  (Time Series Analysis)
    Most econometric methods like ARIMA,ARCH/GARCH family,VAR models
    Smoothing Methods
    Filtering : Kalman, Savitzky Golay etc
    Emprical Mode Decomposition : EMD, CEEMDAN, my own Adaptive methods
    Wavelet Analysis
    Spectral Analysis : Analysis of EEG Data
  (Entropy and Information Theory)
    Permutation Entropy 
    Approximate Entropy 
    Sample Entropy 
    Lempel Ziv Complexity 
    Mutual Information 
    Shannon Entropy
    My own Hybrid Methods
    Multi Scale Entropy  
    

My Experience in Mathematical Optimization

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root{{My Optimization and Evolutionary Algo experience}}
  (Mathematical Optimization)
    Dynamic Programming
    Quadratic Programming
    Convex Optimization
    Combinatorial Optimization
    Bayesian Optimization
    Tree Parzen Optimization (Single and MOPSO)
  (Evolutionary Algorithms)
    Genetic Algorithm
    Particle Swarm Optimization 
    Ant Colony Optimization 
    Differential Evolution 
    Simulated Annealing
    Grey Wolf Optimization 
    Hybrid Memetic Algorithms 
  (Optimization)
    Single Objective Benchmark functions
    Multi Objective Benchmark funtions
    Worked on more than 100+ benchmark functions
    Pick Path Optimization
    Pegion Hole Optimization
  (Metaheuristics)
    Tabu Search
    Greedy Algorithms
    Hill Climbing
    Local Search
    Randomized Algorithms
    Simulated Annealing
    Tabu Search
  (Reinforcement Learning)
   Single Agent RL 
   Multi Agent RL 
   Multi Criteria Optimization with MARL
   Recurrent RL