Arranged according to topics
Machine Learning & Convex Analysis: Video Lectures
Pls Note: Lectures involving Convex Analysis (Convex Optimization) of Machine Learning algorithms are explicitly named as "& Convex Analysis".
 Machine Learning: Linear Regression. Downloadpdf.
 Machine Learning: Locally Weighted Regression
 Machine Learning: Logistic Regression.
 Machine Learning: Probabilistic Interpretation of LeastSquares. Downloadpdf.
 Machine Learning: Exponential Family Distribution & Sufficient Statistics. Downloadpdf.
 Machine Learning: Gaussian Discriminant Analysis
 Machine Learning: NonLinear Regression
 Machine Learning: Linear & LeastSquares Discrimination and Robustness Issues. Downloadpdf.
 Machine Learning: Agglomerative Hierarchical Clustering & Bayesian Information Criterion
 Machine Learning: KullbackLeibler Divergence & Convex Analysis
 Machine Learning: Perceptron Learning & Kernel Perceptron. Downloadpdf file
 Machine Learning: SVM & InDepth Convex Analysis. Downloadpdffile.
Reinforcement Learning: Video Lectures
 My first 5 Lectures Explain first 6 Chapters of "Reinforcement Learning: An Introduction" R. Sutton & A. Barto (1998).
 Reinforcement Learning: An Introduction to Reinforcement & Path Planning
 Reinforcement Learning: Value Functions & Markov Property
 Reinforcement Learning: Iterative Algorithms of Reinforcement Learning
 Reinforcement Learning: Monte Carlo Methods and Introduction to ECAN & RL application
 Reinforcement Learning: Temporal Difference Learning
 Reinforcement Learning: LeastSquares Temporal Difference Learning Downloadpdf
 Reinforcement Learning: FixedPoint Estimation of StateAction Value Function & LeastSquares Policy Iteration Downloadpdf
 Reinforcement Learning: Geometric Analysis of Bellman Residual Minimization & Fixed Point Methods Downloadpdffile
 Reinforcement Learning: Kernelized Value Function Approximation Downloadpdffile
Convex Optimization Applications: Video Presentations
 Audio Reconstruction: An Optimization Approach
 Path Planning: Ellipsoidal Surfaces and Minimum Volume Ellipsoids Downloadpdf
 KullbackLeibler Divergence & Convex Analysis
Path Planning: Video Presentations
 Path Planning: Ellipsoidal Constrained Agent Navigation (My own algorithm) Downloadpdf
Convex & Combinatorial Optimization: Video Lectures
 General Mathematical Optimization: Introduction. Downloadpdf.
 Unconstrained Minimization: Theoretical Analysis of Stopping Criterion & Condition Number. Downloadpdf.
 Unconstrained Minimization: Backtracking Line Search & Gradient Descent. Downloadpdf.
 Unconstrained Minimization: Convergence Analysis of Gradient Descent using LineSearch. Downloadpdf.
 Unconstrained Minimization: Steepest Descent Methods and Convergence Analysis Under Backtracking. Downloadpdf.
 Unconstrained Minimization: Newton Method Using Backtracking Line Search. Video Lec  Coming Soon. DownloadPdf.
Mathematical Optimization: Lectures  (these will be removed in future)
 Mathematical Optimization: Introduction and General Overview  (in 3 parts, embedded from youtube):
Part1
Part2
Part3
 Mathematical Optimization: Line Search Methods  (in 3 parts, uploaded on youtube, and embedded in searchingeye):
Part1
Part2
Part3
Image Processing: Applications
 Image Processing: Hand Finger Detection Using Image Processing
My Course: ARL10/11 : Advanced Reinforcement Learning2010/11.
CCO10/11: Convex & Combinatorial Optimization2010/11.
My CLTI: CFMAS: SP11 : Coalition Formation in MultiAgent Systems: Strategic Planning2011.
My Course: MLR : Machine Learning Repertoire.

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ARL10/11
CCO10/11
MLR
CFMAS:SP11
SSMS
Machine Learning Channel
Reinforcement Learning Channel
Following are coming soon:
These lectures would be more practical
 Decomposition Methods
 Averaging in Subgradient Methods for Optimization
 Steepest Descent Methods  Uploaded
 Newton′s Method
 SelfConcordance
 Network Flow: Linear Programming
 Support Vector Clustering
 Support Vector Regression
Except MILP, MIQP and Lower Bound Computation, if there is any special topic that you would like me to explain in Machine Learning, Reinforcement Learning or Convex Optimization channel of SearchingEye, then please send mail to sanjeev.searchingeye@gmail.com
