Advanced AI: Techniques, Applications, and Ethics
Advanced AI: Techniques, Applications, and Ethics
What you’ll learn
Skills you’ll gain
AI and ML are often discussed as one technology, but in reality, they encompass various algorithms with distinct purposes. This course dives into the field of AI, focusing on machine learning and its advanced applications that are not commonly explored elsewhere. Starting with a clear understanding of AI and ML, you will embark on an organized survey of machine learning techniques. You will learn to articulate and think critically about these technologies, discover reliable sources for trained models, and gain the skills to build your own models. Additionally, the course emphasizes the design of ethical and human-centric AI systems, empowering you to create AI solutions that prioritize social responsibility. Explore the fascinating world of advanced AI methods, concepts, and applications, including reinforcement learning, and enhance your ability to leverage these technologies effectively.
Syllabus
Download syllabus-
1
Demystifying AI and ML With many different definitions for what AI and ML are, it's important for you to feel clear on how they'll be defined in this course. 1m
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2
Types of machine learning There are many different types of machine learning including supervised, unsupervised, semi-supervised, reinforcement learning and causal/structural learning. 2m
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3
Types of artificial intelligence Some problems are sole decision problems (is it a cow or a donkey in the picture) and some are more like games (trading algorithms or smart parking algorithms). 2m
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1
Purposes of algorithms Algorithms can and do help humanity, but they might also and have already hurt humanity. 2m
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2
Solving regression problems When you predict a quantity, it is important to know what algorithms are the best and why. 2m
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3
Classification and detection problems Sometimes a machine learning engineer or data scientist needs to predict a class or build an algorithm for object detection. 3m
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1
Demystifying relationships in data For many problems, especially in policy-making and healthcare, it is important to not just predict, using many correlations, but to understand causation and to understand other types of substantive relationships in the data. 7m
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2
Integrating knowledge graphs Knowledge Graphs give machines common sense; without them they are limited to learning correlations in the data. 2m
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3
Leveraging transfer learning When you have only a few data points, relatively speaking, you are not necessarily stuck but you might be able to use transfer learning to make state of the art inference. 2m
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1
Generating sensible language utterances Conversational interfaces are clearly part of the future development in app building. 3m
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2
Building conversational experiences According to Gartner Quadrant, conversational interfaces will soon replace most apps. 3m
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1
Building competitive games Reinforcement learning is a great technique to build competitive games. 3m
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2
Building cooperative games Building cooperative games is not only a fun application of algorithms, but it can also be a serious and useful application. 3m
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1
Mitigating bias in ML Bias is often cited as one of the major ethical failings of machine learning models. 4m
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2
Bias conflicting with privacy Privacy is another important common ethical failing, and protocols around this failing have now been encoded into law. 3m
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3
Identifying conflicting ethics When examining AI/ML ethics, it's important to ask "what ethic?" instead of "is it ethical?". 4m
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4
Avoiding ethically paternalistic apps Paternalistic apps are one of the common ethical challenges that arise when building apps. 6m
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5
Integrating ethical design systems The Value Sensitive Design is an important framework for designing systems that embody ethical values. 3m
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6
Creating capability sensitive designs Capability Sensitive Design is able to account for human diversity, and it's able to counter injustices that manifest in technology design. 6m
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1
Reinforce your learning Thank you for watching this course! 1m
Certificate
Certificate of Completion
Awarded upon successful completion of the course.
Instructor
Johannes Castner
Johannes is a renowned data scientist and machine learning engineer whose journey from academic adversity to professional excellence is truly inspiring. Starting without a high school diploma, he excelled at Santa Monica College and then graduated magna cum laude from Columbia University. Johannes has worked as a research assistant at the Federal Reserve Bank of Boston and took graduate courses at Harvard University. His contributions to a Bayesian Network Python library led to a senior data scientist role at eBay. Now, as the founder of his startup, CollectiWise, and an independent consultant, Johannes brings unparalleled expertise and innovation to the field of data science and machine learning.
Johannes Castner
AI/ML Engineer and Speaker
Accreditations
Link to awards
How GoSkills helped Chris
I got the promotion largely because of the skills I could develop, thanks to the GoSkills courses I took. I set aside at least 30 minutes daily to invest in myself and my professional growth. Seeing how much this has helped me become a more efficient employee is a big motivation.
