Social And Emotional Learning Courses

MITCBMM

Deep Learning Tutorial

Explore deep learning fundamentals, from supervised learning to convolutional networks, with practical examples and historical context. Gain insights into advanced concepts and applications.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

MITCBMM

Fit Without Fear - An Over-Fitting Perspective on Modern Deep and Shallow Learning

Explore over-fitting in modern machine learning, challenging conventional wisdom and presenting kernel methods as a competitive alternative to deep learning. Insights on interpolation, generalization, and stochastic gradient descent.

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

MITCBMM

Machine Learning and AI for the Sciences - Towards Understanding

Explore ML and AI applications in sciences, focusing on neuroscience, medicine, and physics. Learn about extracting insights from ML models to enhance understanding in fields like quantum chemistry.

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

MITCBMM

Reinforcement Learning

Explore reinforcement learning concepts and their applications in psychology and neuroscience. Hands-on exercises demonstrate how algorithms explain animal learning and dopamine neuron firing.

• YouTube
• 1 hour 10 minutes
• Self-Paced
• Free Video

MITCBMM

Machine Learning Techniques and Applications in Finance, Healthcare and Recommendation Systems

Explore state-of-the-art machine learning techniques and their applications in finance, healthcare, and recommendation systems, focusing on ensemble methods, collaborative filtering, and portfolio construction.

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

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