Information Visualization, Visual Display of Quantitative Information, Power of Representation, Data-Ink and Graphical Redesign, Data Density, Interactive Data Visualization for the Web. Scalable, Versatile and Simple Constrained Graph Layout, Visualization of Adjacency Relations in Hierarchical Data 

Theory, Experimentation and the Application to the Development of Graphical Models, Layering Interactive Dynamics for Visual Analysis, Animated Transitions in Statistical Data Graphics Effectiveness of Animation in Trend Visualization

Lexical Analysis: Techniques for tokenizing input code. Syntax Analysis: Parsing strategies to understand code structure. Semantic Analysis: Ensuring code adheres to language rules. Intermediate Code Generation: Translating source code into an intermediate representation. Optimization: Enhancing the intermediate code for performance. Code Generation: Producing machine-level code for execution. Run-Time Systems: Managing resources during program execution.

Introductory Concepts: Perceptron, multilayer perceptron, deep learning as composite functions, Loss functions, activation functions, backpropagation, deep learning frameworks (e.g., TensorFlow, PyTorch, Keras). Optimization Algorithms and Regularization Techniques; Convolutional Neural Networks (CNNs): architecture, pretrained models, transfer learning, backpropagation; Sequence Modeling: Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), Gated Recurrent Units (GRUs), attention mechanisms, and their backpropagation; Encoderdecoder models.