Social And Emotional Learning Courses

MITCBMM

Unsupervised Learning of Spoken Language with Visual Context

Explores unsupervised learning of spoken language using visual context, aiming to develop ASR capabilities for more languages through audio-visual embedding and clustering techniques.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

MITCBMM

Learning with Symmetry and Invariance for Speech Perception

Explore symmetry and invariance in speech perception, focusing on acoustic variability, feedforward models, and invariant representations. Learn about word orbits, phone representations, and acoustic modeling techniques.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

MITCBMM

Learning About the Physical World - Comparing Humans, Deep Neural Networks, and Probabilistic Programs

Explore intuitive physics and psychology in human cognition, comparing it with AI approaches like deep neural networks and probabilistic programs for understanding the physical world.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

MITCBMM

Learning & Cortex - I-Theory

Explore principles of data representation, invariance, and selectivity in deep neural networks, focusing on convolutional architectures and their theoretical foundations.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

MITCBMM

Connections Between Physics and Deep Learning

Explore the fascinating intersections of physics and deep learning, from neural networks to scale-invariant behavior, with insights from MIT's Max Tegmark.

• YouTube
• 52 minutes
• Self-Paced
• Free Video

MITCBMM

Learning in Recurrent Neural Networks

Explore recurrent neural networks, covering continuous and spiking models, effective connectivity, and philosophical implications in computational neuroscience.

• YouTube
• 1 hour 17 minutes
• Self-Paced
• Free Video

MITCBMM

Prof. Lorenzo Rosasco- Multiple Kernel Learning

Explore multiple kernel learning techniques with Prof. Lorenzo Rosasco, covering overlapping groups, vector notation, proxy operators, and more in this comprehensive lecture.

• YouTube
• 1 hour 25 minutes
• Self-Paced
• Free Video

MITCBMM

The Learning Problem and Regularization

Explore the foundations of machine learning with Prof. Poggio, covering key concepts like generalization, consistency, and regularization in solving learning problems.

• YouTube
• 1 hour 19 minutes
• Self-Paced
• Free Video

MITCBMM

Kristen Grauman - Learning the Right Thing with Visual Attributes

Explore visual attribute learning techniques, including shared properties, transfer learning, and local approaches for fine-grained attribute differences in computer vision.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

MITCBMM

Tony Cohn - Simultaneously Learning and Grounding Object Features and Spatial Relations

Explore spatial information, qualitative relations, and inductive logic programming to understand object features and spatial relationships in various contexts.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

MITCBMM

Theories for Intelligence - Lorenzo Rosasco: Learning Theory

Explore machine learning fundamentals, learning theory, and supervised learning techniques like nearest neighbor algorithms, with practical exercises and mathematical insights.

• YouTube
• 1 hour 40 minutes
• Self-Paced
• Free Video

Abhishek Thakur

Deep Learning for Survival Analysis

Explore deep learning models for survival analysis, applying them to predict time-to-event outcomes in various fields like credit, machinery, and healthcare with Louise Ferbach.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Abhishek Thakur

An Introduction to Transfer Learning in NLP and HuggingFace

Explore breakthroughs in NLP through transfer learning and Transformers. Discover HuggingFace's open-source tools for democratizing NLP research and development.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Abhishek Thakur

What Are CycleGANs? - A Novel Deep Learning Tool in Pathology

Explore CycleGANs for unpaired image conversion, including applications in pathology and microscopy. Learn model architecture, training, and inference through code demos and real-world examples.

• YouTube
• 1 hour 28 minutes
• Self-Paced
• Free Video

Abhishek Thakur

How to Train a Deep Learning Model Using Docker

Learn to train deep learning models using Docker, NVIDIA runtimes, and PyTorch for melanoma classification on a single GPU, with practical code implementation and troubleshooting tips.

• YouTube
• 21 minutes
• Self-Paced
• Free Video