COURSE OVERVIEW

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AI engineering involves designing, developing, and implementing artificial intelligence systems and technologies. It combines principles from computer science, data science, and software engineering to create intelligent systems that can learn from data, make decisions, and solve complex problems. .

Linear Algebra:

  • Vectors and matrices
  • Eigenvalues and eigenvectors
  • Singular value decomposition (SVD)

    • Calculus:

  • Differentiation and integration
  • Optimization methods (gradient descent, Newton's method)

    • Probability and Statistics:

  • Probability distributions(Gaussian, Bernoulli, etc.)
  • Hypothesis testing and confidence intervals
  • Bayesian statistics

    • Python Programming:

  • Data structures (lists,dictionaries, tuples, sets)
  • Functions, modules, packages
  • Exception handling, file I/O

    • Introduction to Machine Learning:

  • Supervised learning,unsupervised learning,reinforcement learning
  • Model evaluation metrics(accuracy, precision, recall, F1-score)

    • Regression

  • Decision trees, random forests, support vector machines (SVM)

    • Clustering:

  • K-means clustering, hierarchical clustering

    • Neural Networks Basics:

  • Perceptrons, activation functions (sigmoid, ReLU, tanh)
  • Feedforward neural networks

    • Deep Learning Frameworks:

  • TensorFlow: Basics of tensors, building neural networks
  • PyTorch: Tensors, autograd, building dynamic neural networks

    • Convolutional Neural Networks (CNNs):

  • Architecture, filters, pooling layers
  • Applications in computer vision

    • Recurrent Neural Networks (RNNs):

  • Architecture, LSTM, GRU
  • Applications in sequence modeling (e.g., NLP)

    • Text Preprocessing:

  • Tokenization, stemming, lemmatization
  • Stopword removal, text normalization

    • NLP Techniques:

  • Named Entity Recognition (NER)
  • Sentiment analysis, text classification
  • Language modeling, word embeddings (Word2Vec, GloVe)

    • Transformers and Advanced NLP Models:

  • Attention mechanisms, BERT, GPT

    • Image Processing Basics:

  • Image representation (pixels, RGB channels)
  • Image transformations (resizing, cropping)

    • Computer Vision Techniques:
  • Object detection, image segmentation
  • Feature extraction using CNNs

    • Applications:

  • Facial recognition, autonomous driving, medical imaging

    • Ethical Considerations:

  • Bias, fairness, transparency, interpretabilityM
  • Privacy concerns in AI applications

    • AI Regulations and Guidelines:

  • GDPR, AI ethics guidelines (IEEE, ACM)
  • Impact of AI on society and employment

    • Reinforcement Learning:

  • Markov decision processes, Q- learning, policy gradients

    • Generative Adversarial Networks (GANs):

  • Architecture, applications in generating realistic data

    • Model Deployment and Productionization:

  • Docker, Kubernetes for containerization
  • Deployment strategies, model monitoring